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100644 index 0000000000000000000000000000000000000000..0299c6272a6e3596c17bcc194ff5576ff754269d --- /dev/null +++ b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/evidence/accessory_eyeglasses_a01/reconstruction-2/harbor-trajectory-4.json @@ -0,0 +1,1985 @@ +{ + "schema_version": "ATIF-v1.7", + "session_id": "01a15a73-901f-77f3-a50e-b9346a6507c2", + "agent": { + "name": "pi", + "version": "0.85.1", + "model_name": "harbor-endpoint/gpt-6.1-sol" + }, + "steps": [ + { + "step_id": 1, + "timestamp": "2026-10-20T17:54:47.629Z", + "source": "user", + "message": [ + { + "type": "text", + "text": "\nReconstruct the supplied object as /output/asset.usd.\nNominal dimensions in canonical metres XYZ: [0.14458004385068107, 0.1743206977770294, 0.05692390352487564]\nRead /task/contract.md, /task/tool-docs.md and /task/scoring-policy.json. Identify the rigid parts, joints and their motion before authoring, then reconstruct appearance and mechanical structure. Use Python/PXR. Do not use Blender. Validate and inspect the NVIDIA findings before submitting.\n" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_1.png" + } + } + ], + "extra": { + "pi_event_id": "e2254711", + "pi_parent_id": "7fe10417" + } + }, + { + "step_id": 2, + "timestamp": "2026-10-20T17:54:50.379Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_mFtgREvFNBhNCEOUMWu1ArER|fc_09ef6deae62cdba9016ad7aae9ea3c87d28b3573761b7fd605", + "function_name": "read", + "arguments": { + "path": "/task/contract.md", + "offset": 1, + "limit": 2000 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_mFtgREvFNBhNCEOUMWu1ArER|fc_09ef6deae62cdba9016ad7aae9ea3c87d28b3573761b7fd605", + "content": "# Reconstruction contract\n\nReconstruct the supplied object at /output/asset.usd using Python/PXR. Include\nits local USD and texture dependencies and place authoring scripts in /tmp.\nRead the supplied dimensions, image and scoring-policy.json.\n\nThe evaluation reports three independent scores:\n\n- Formal fidelity: NVIDIA SimReady-Foundation Robotics-Prop 4.0.0. Each of its\n 46 mandatory requirements counts once, with 95 * passed / 46 points and five\n further points when all pass. The 51 optional requirements are reported separately.\n- Visual fidelity: reference-scale shape, colours, textures and surface appearance.\n- Kinematic fidelity: the object's connected rigid parts, joint types, hinge or\n sliding axes, attachment locations and allowed ranges of motion.\n\nIdentify the object's parts and their mechanical relationships before authoring.\nKeep parts that move relative to one another as distinct rigid bodies and author\nthe corresponding UsdPhysics joints. Specify each joint's connected bodies,\nlocal positions, local orientations, axis and appropriate motion limits. For\nexample, folding eyewear needs hinged temples rather than one rigid mesh.\nUse fixed joints for genuine fixed attachments and keep genuinely rigid objects\nrigid. Base this structure on the supplied observations and the object's function.\n\nCheck that joint frames meet at each attachment, that movable parts can travel\nthrough their intended range and that the bodies and joints have the appropriate\nenabled and kinematic states. Preserve the observed shape and materials while\nconstructing this mechanical structure.\n\nUse validate_candidate to inspect the NVIDIA findings and repair the candidate.\nThe final submission freezes the authored files for evaluation.", + "extra": { + "tool_name": "read", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 2668, + "completion_tokens": 30, + "cached_tokens": 0, + "extra": { + "cache_write_tokens": 2665, + "reasoning_tokens": 0, + "pi_total_tokens": 2698 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "fe3679f1", + "pi_parent_id": "e2254711", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aae83aa887d2be7dfa1ada3c5bc1", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 3, + "timestamp": "2026-10-20T17:54:53.623Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_Xw1gMLXCYB7SfW0TRGp1cI6B|fc_09ef6deae62cdba9016ad7aaed622887d2a9c2f2e562ded8bb", + "function_name": "read", + "arguments": { + "path": "/task/tool-docs.md", + "offset": 1, + "limit": 2000 + } + }, + { + "tool_call_id": "call_hhAweAeNjyo5WivbQXRJ0XWr|fc_09ef6deae62cdba9016ad7aaed623887d2b53fc245ef43ac8e", + "function_name": "read", + "arguments": { + "path": "/task/scoring-policy.json", + "offset": 1, + "limit": 2000 + } + }, + { + "tool_call_id": "call_GR3ib9hoYmaEvo7o0Mm99uSJ|fc_09ef6deae62cdba9016ad7aaed624887d2bb3f03750263a17a", + "function_name": "mcp", + "arguments": { + "search": "validate_candidate" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_Xw1gMLXCYB7SfW0TRGp1cI6B|fc_09ef6deae62cdba9016ad7aaed622887d2a9c2f2e562ded8bb", + "content": "# Authoring tools\n\nget_task_thumbnail returns the reference image. validate_candidate accepts {\"request_id\":\"check-1\"} and returns the current score, remaining validation calls and nvidia_validator_report.file_path. Search that JSON file with rg for a requirement ID or candidate prim path. get_validation_report reads a selected range of the same report.\n\nUse a new request ID after each repair. submit_asset accepts {\"request_id\":\"final\"} and stops authoring. The model has 64 inference requests, with provider-native output limits. Captures appear in the final report.", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "call_hhAweAeNjyo5WivbQXRJ0XWr|fc_09ef6deae62cdba9016ad7aaed623887d2b53fc245ef43ac8e", + "content": "{\n \"benchmark_output_token_cap\": null,\n \"composite_scores\": {\n \"formal\": \"SimReady Robotics-Prop mandatory requirements\",\n \"kinematic\": \"joint recovery and allowed relative motion correspondence\",\n \"visual\": \"geometric mean of shape and appearance material correspondence\"\n },\n \"limits\": {\n \"agent_seconds\": 231000,\n \"final_seconds\": 600,\n \"inference_requests\": 64,\n \"preview_requests\": 12,\n \"preview_seconds\": 90,\n \"validation_calls\": 5,\n \"validation_seconds\": 120,\n \"views_per_preview\": 4\n },\n \"model_call_budget\": 64,\n \"reward_reporting\": {\n \"formula\": \"95 * passed / fixed_applicable + 5_if_all_pass\",\n \"harbor_key\": \"simready_reward\",\n \"scale\": [\n 0,\n 100\n ]\n },\n \"semantic_labels\": {\n \"criterion\": \"mandatory NVIDIA SL IDs only\",\n \"required\": true,\n \"scoring\": true\n },\n \"validator_scoring\": {\n \"all_pass_bonus_percent\": 5,\n \"applicability\": \"fixed_profile_and_task_before_candidate\",\n \"applicable_requirement_ids\": [\n \"com.nvidia.simready.HI.010\",\n \"com.nvidia.simready.NP.002\",\n \"com.nvidia.simready.NP.003\",\n \"com.nvidia.simready.NP.004\",\n \"com.nvidia.simready.NP.005\",\n \"com.nvidia.simready.NP.007\",\n \"com.nvidia.simready.NP.008\",\n \"com.nvidia.simready.RB.001\",\n \"com.nvidia.simready.RB.007\",\n \"com.nvidia.simready.RB.010\",\n \"com.nvidia.simready.RB.COL.001\",\n \"com.nvidia.simready.RB.COL.002\",\n \"com.nvidia.simready.RB.COL.003\",\n \"com.nvidia.simready.SL.001\",\n \"com.nvidia.simready.SL.002\",\n \"com.nvidia.simready.SL.003\",\n \"com.nvidia.simready.SL.MAT.001\",\n \"com.nvidia.simready.SL.QCODE.001\",\n \"com.nvidia.simready.SL.TIME.001\",\n \"com.nvidia.simready.SR.002\",\n \"com.nvidia.simready.SR.004\",\n \"com.nvidia.simready.UN.006\",\n \"com.nvidia.simready.UN.007\",\n \"com.nvidia.simready.VG.008\",\n \"com.nvidia.simready.VG.025\",\n \"com.nvidia.simready.VG.MESH.001\",\n \"com.nvidia.simready.VM.MAT.001\",\n \"com.nvidia.simready.VM.PS.002\",\n \"com.nvidia.simready.VM.TEX.001\",\n \"com.nvidia.simready.VM.TEX.003\",\n \"com.nvidia.usd.AA.001\",\n \"com.nvidia.usd.AA.002\",\n \"com.nvidia.usd.HI.001\",\n \"com.nvidia.usd.HI.003\",\n \"com.nvidia.usd.HI.004\",\n \"com.nvidia.usd.RB.003\",\n \"com.nvidia.usd.RB.005\",\n \"com.nvidia.usd.RB.009\",\n \"com.nvidia.usd.RB.COL.004\",\n \"com.nvidia.usd.VG.002\",\n \"com.nvidia.usd.VG.014\",\n \"com.nvidia.usd.VG.027\",\n \"com.nvidia.usd.VG.028\",\n \"com.nvidia.usd.VG.029\",\n \"com.nvidia.usd.VM.BIND.001\",\n \"com.nvidia.usd.VM.PS.001\"\n ],\n \"base_credit_percent\": 95,\n \"early_stop_penalty_percent\": 0,\n \"optional_requirements\": \"report_only\",\n \"policy_id\": \"NVIDIA-Robotics-Prop-4.0.0-mandatory-95-plus-5-v1\",\n \"process_shaping_percent\": 0,\n \"profile\": {\n \"id\": \"Robotics-Prop\",\n \"version\": \"4.0.0\"\n },\n \"profile_inventory_sha256\": \"5a8dccd559cd3625025cedd08f7e2c174df32b8bbe0b17da9c94a588c86da13c\",\n \"report_only_mandatory_ids\": [],\n \"schema\": \"simready-validator-scoring-policy-v1\",\n \"scope\": \"all_mandatory_profile\",\n \"scoring_unit\": \"one_mandatory_requirement_ID\",\n \"stop_bonus_percent\": 0,\n \"unknown\": \"null_score_fixed_denominator\"\n }\n}", + "extra": { + "tool_name": "read", + "is_error": false + } + }, + { + "source_call_id": "call_GR3ib9hoYmaEvo7o0Mm99uSJ|fc_09ef6deae62cdba9016ad7aaed624887d2bb3f03750263a17a", + "content": "Found 1 tool matching \"validate_candidate\":\n\nbenchmark_validate_candidate\n Validate a frozen candidate and return NVIDIA findings. Each new request consumes one validation call. Repeating a request ID returns its existing result.\n\n Shape:\n { request_id: string; }", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 3036, + "completion_tokens": 89, + "cached_tokens": 2665, + "extra": { + "cache_write_tokens": 368, + "reasoning_tokens": 0, + "pi_total_tokens": 3125 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7ba81ec4", + "pi_parent_id": "94285cf4", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aaeafce487d28b7b0ac1a4c3cdab", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 4, + "timestamp": "2026-10-20T17:55:00.840Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "The object is a pair of browline sunglasses. I\u2019ll model the front frame, bridge, lenses, and nose pads as one rigid assembly, with two separate temples hinged at the outer corners. Each temple will rotate about a near-vertical axis from the open position shown to a folded position behind the lenses.", + "reasoning_content": "**Designing glasses dimensions**\n\nI need to build a pair of glasses and first, I require the geometry dimensions: canonical XYZ width is .14458, depth .17432, and height .05692. The Z axis is up while the front is along the negative Y. The arms should extend in the positive Y direction. The frame should be positioned in the XZ plane near Y -0.077, with tips around Y .09. The lenses' height is .047, while the tips curve down. I need to ensure the design has visually detailed features like black speckled browline, round square lenses with a bronze lower rim, and blue-black gradient lenses. I\u2019m also curious if PXR is installed.", + "tool_calls": [ + { + "tool_call_id": "call_t64x15xLgXaz0uhFr0yAYtpj|fc_09ef6deae62cdba9016ad7aaf3f2d887d284b5b711a8d2f05b", + "function_name": "bash", + "arguments": { + "command": "ls -la /output && python - <<'PY'\nfrom pxr import Usd,UsdGeom,UsdPhysics\nprint('pxr ok')\nPY", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_t64x15xLgXaz0uhFr0yAYtpj|fc_09ef6deae62cdba9016ad7aaf3f2d887d284b5b711a8d2f05b", + "content": "total 0\ndrwxrwxrwx 2 actor actor 40 Oct 20 17:54 .\ndrwxr-xr-x 1 root root 56 Oct 20 17:54 ..\npxr ok", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4377, + "completion_tokens": 229, + "cached_tokens": 3033, + "extra": { + "cache_write_tokens": 1341, + "reasoning_tokens": 102, + "pi_total_tokens": 4606 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "556a8a30", + "pi_parent_id": "bf3fb399", + "provider": 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asset....\n- benchmark_get_validation_report - Read a requested range of a prior NVIDIA report....", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4667, + "completion_tokens": 18, + "cached_tokens": 4374, + "extra": { + "cache_write_tokens": 290, + "reasoning_tokens": 0, + "pi_total_tokens": 4685 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "69ef8a94", + "pi_parent_id": "3f764115", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aaf5d22887d2967d440177b69c5c", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 6, + "timestamp": "2026-10-20T17:55:08.383Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_XSvmxEDSOLDERbtt1mAbskWc|fc_09ef6deae62cdba9016ad7aafaa37487d28d33ff90835b5473", + "function_name": "bash", + "arguments": { + "command": "which usdrecord || true; python - <<'PY'\nimport importlib.util\nfor p in ['numpy','PIL','trimesh','scipy','OpenGL']:\n print(p,bool(importlib.util.find_spec(p)))\nfrom pxr import Usd\nprint(Usd.GetVersion())\nPY", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_XSvmxEDSOLDERbtt1mAbskWc|fc_09ef6deae62cdba9016ad7aafaa37487d28d33ff90835b5473", + "content": "numpy True\nPIL True\ntrimesh False\nscipy False\nOpenGL False\n(0, 26, 8)", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4759, + "completion_tokens": 111, + "cached_tokens": 4664, + "extra": { + "cache_write_tokens": 92, + "reasoning_tokens": 21, + "pi_total_tokens": 4870 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "15359c00", + "pi_parent_id": "30470e3d", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": 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Tf.Type.Unknown\nPhysxCollisionAPI Tf.Type.Unknown", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4910, + "completion_tokens": 1118, + "cached_tokens": 4756, + "extra": { + "cache_write_tokens": 151, + "reasoning_tokens": 1034, + "pi_total_tokens": 6028 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9baa1753", + "pi_parent_id": "94539724", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aafd2c7087d2a6b011e20e293c06", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 8, + "timestamp": "2026-10-20T17:57:03.178Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_t9wRzrhPE9a0JUF8E9yuVz0o|fc_09ef6deae62cdba9016ad7ab2596c487d2b97d9f4072561162", + "function_name": "write", + "arguments": { + "path": "/tmp/build_asset.py", + "content": "from pxr import Usd, UsdGeom, UsdShade, UsdPhysics, UsdSemantics, Gf, Sdf, Vt\nimport numpy as np, math, os, random\nfrom PIL import Image,ImageDraw,ImageFont,ImageFilter\nos.makedirs('/output/textures',exist_ok=True)\nrandom.seed(76)\n# Acetate is black with small irregular inclusions, not a uniform plastic.\ndef texture(path,label=False):\n w,h=2048,512\n rng=np.random.default_rng(132)\n grain=rng.normal(0,1.2,(h,w,1))\n a=np.clip(np.array([40,41,43])+grain,0,255).astype('uint8')\n im=Image.fromarray(a,'RGB');d=ImageDraw.Draw(im)\n cols=[(169,161,146),(206,200,185),(99,106,114),(27,115,153),(47,144,180),(183,143,60),(108,99,84)]\n for i in range(255):\n x=random.randrange(w);y=random.randrange(h);r=random.choice([1,1,2,2,3,4,6,8]);rx=r*random.uniform(.6,2.3);ry=r*random.uniform(.4,1.4)\n pts=[]\n for j in range(7):\n t=j*math.tau/7;f=random.uniform(.45,1.25);pts.append((x+rx*f*math.cos(t),y+ry*f*math.sin(t)))\n d.polygon(pts,fill=random.choice(cols))\n if r>5:d.line([(x-r,y),(x+rx*.6,y-ry*.2)],fill=(52,53,54),width=2)\n if label:\n font=ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',20)\n for y in [116,372]:\n d.rounded_rectangle((108,y-3,500,y+24),radius=3,fill=(36,37,39))\n d.text((115,y),'EISENSTEIN OPTIK',font=font,fill=(167,170,174))\n im.save(path)\ntexture('/output/textures/acetate.png');texture('/output/textures/temples.png',True)\nstage=Usd.Stage.CreateNew('/output/asset.usd')\nUsdGeom.SetStageUpAxis(stage,UsdGeom.Tokens.z);UsdGeom.SetStageMetersPerUnit(stage,1.0)\nUsdGeom.SetStageKilogramsPerUnit(stage,1.0) if hasattr(UsdGeom,'SetStageKilogramsPerUnit') else None\nroot=UsdGeom.Xform.Define(stage,'/Sunglasses');stage.SetDefaultPrim(root.GetPrim())\nroot.GetPrim().SetAssetInfoByKey('name','Speckled browline sunglasses')\nroot.GetPrim().SetAssetInfoByKey('identifier',Sdf.AssetPath('asset.usd'))\nroot.GetPrim().SetCustomDataByKey('description','Browline sunglasses with glazed lenses, alloy bridge and two folding acetate temples')\nscene=UsdPhysics.Scene.Define(stage,'/Sunglasses/PhysicsScene');scene.CreateGravityDirectionAttr((0,0,-1));scene.CreateGravityMagnitudeAttr(9.81)\nUsdGeom.Scope.Define(stage,'/Sunglasses/Looks')\ndef semantic(prim,label):\n api=UsdSemantics.LabelsAPI.Apply(prim,'class');api.CreateLabelsAttr([label])\n prim.SetCustomDataByKey('semantic:class',label)\nsemantic(root.GetPrim(),'sunglasses')\ndef material(name,color,metal=0,rough=.3,texture=None):\n m=UsdShade.Material.Define(stage,'/Sunglasses/Looks/'+name);s=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('Surface'));s.CreateIdAttr('UsdPreviewSurface')\n s.CreateInput('diffuseColor',Sdf.ValueTypeNames.Color3f).Set(color);s.CreateInput('roughness',Sdf.ValueTypeNames.Float).Set(rough);s.CreateInput('metallic',Sdf.ValueTypeNames.Float).Set(metal)\n s.CreateInput('ior',Sdf.ValueTypeNames.Float).Set(1.5)\n if texture:\n st=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('UV'));st.CreateIdAttr('UsdPrimvarReader_float2');st.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('st')\n t=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('ColorTexture'));t.CreateIdAttr('UsdUVTexture');t.CreateInput('file',Sdf.ValueTypeNames.Asset).Set(Sdf.AssetPath(texture));t.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('sRGB');t.CreateInput('st',Sdf.ValueTypeNames.Float2).ConnectToSource(st.ConnectableAPI(),'result');s.GetInput('diffuseColor').ConnectToSource(t.ConnectableAPI(),'rgb')\n m.CreateSurfaceOutput().ConnectToSource(s.ConnectableAPI(),'surface');return m\nacetate=material('FleckedBlackAcetate',(.025,.026,.028),.05,.28,'textures/acetate.png')\ntemplemat=material('AcetateWithMakerMark',(.025,.026,.028),.05,.28,'textures/temples.png')\nmetal=material('ChampagneGunmetal',(.34,.255,.217),.86,.23)\nchrome=material('PolishedHingeAlloy',(.42,.45,.48),.9,.2)\npadmat=material('DarkSilicone',(.012,.014,.017),0,.4)\nlens=material('SmokeBlueOpticalGlass',(.015,.02,.03),.15,.13)\nls=UsdShade.Shader.Get(stage,lens.GetPath().AppendChild('Surface'))\nreader=UsdShade.Shader.Define(stage,lens.GetPath().AppendChild('LensTint'));reader.CreateIdAttr('UsdPrimvarReader_float3');reader.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('displayColor');ls.GetInput('diffuseColor').ConnectToSource(reader.ConnectableAPI(),'result')\nls.CreateInput('clearcoat',Sdf.ValueTypeNames.Float).Set(.65);ls.CreateInput('clearcoatRoughness',Sdf.ValueTypeNames.Float).Set(.09)\ndef body(name,pos,mass):\n x=UsdGeom.Xform.Define(stage,'/Sunglasses/'+name);x.AddTranslateOp().Set(Gf.Vec3d(*pos));rb=UsdPhysics.RigidBodyAPI.Apply(x.GetPrim());rb.CreateRigidBodyEnabledAttr(True);rb.CreateKinematicEnabledAttr(False)\n ma=UsdPhysics.MassAPI.Apply(x.GetPrim());ma.CreateMassAttr(mass);ma.CreateCenterOfMassAttr((0,0,0));semantic(x.GetPrim(),'sunglasses_'+name.lower());return x\nfront=body('FrontAssembly',(0,0,0),.025)\nleft=body('LeftTemple',(-.069,-.077,.050),.006)\nright=body('RightTemple',(.069,-.077,.050),.006)\ndef mesh(name,pts,faces,mat,uv=None,colors=None,collision=False):\n m=UsdGeom.Mesh.Define(stage,name);m.CreatePointsAttr(pts);m.CreateFaceVertexCountsAttr([len(f) for f in faces]);m.CreateFaceVertexIndicesAttr([i for f in faces for i in f]);m.CreateSubdivisionSchemeAttr('none');m.CreateDoubleSidedAttr(False)\n ar=np.array(pts);m.CreateExtentAttr([tuple(ar.min(0)),tuple(ar.max(0))])\n # Explicit smooth vertex normals, with consistently outward oriented faces.\n ns=np.zeros_like(ar)\n for f in faces:\n n=np.cross(ar[f[1]]-ar[f[0]],ar[f[2]]-ar[f[0]])\n for j in f:ns[j]+=n\n ns/=np.maximum(np.linalg.norm(ns,axis=1)[:,None],1e-20)\n m.CreateNormalsAttr([tuple(v) for v in ns]);m.SetNormalsInterpolation('vertex')\n if uv is not None:UsdGeom.PrimvarsAPI(m).CreatePrimvar('st',Sdf.ValueTypeNames.TexCoord2fArray,'vertex').Set(uv)\n if colors is not None:UsdGeom.PrimvarsAPI(m).CreatePrimvar('displayColor',Sdf.ValueTypeNames.Color3fArray,'vertex').Set(colors)\n UsdShade.MaterialBindingAPI.Apply(m.GetPrim()).Bind(mat)\n if collision:\n UsdPhysics.CollisionAPI.Apply(m.GetPrim()).CreateCollisionEnabledAttr(True);UsdPhysics.MeshCollisionAPI.Apply(m.GetPrim()).CreateApproximationAttr('convexHull')\n return m\n# Swept, softly squared acetate rods and smoothly round alloy wire.\ndef sweep(path,centers,widths,heights,mat,closed=False,sides=16,square=False,collision=False,uoffset=0):\n c=np.array(centers);n=len(c);pts=[];uv=[];length=np.r_[0,np.cumsum(np.linalg.norm(np.diff(c,axis=0),axis=1))];total=length[-1]\n for i,p in enumerate(c):\n tangent=c[(i+1)%n]-c[(i-1)%n] if closed else c[min(i+1,n-1)]-c[max(i-1,0)]\n tangent/=np.linalg.norm(tangent)\n # stable sweep frame using Y axis for the front frame, X for temples\n axis=np.array([0.,1.,0.]) if abs(tangent[1])<.8 else np.array([1.,0.,0.])\n e1=axis-np.dot(axis,tangent)*tangent;e1/=np.linalg.norm(e1);e2=np.cross(tangent,e1)\n for j in range(sides+1):\n a=math.tau*j/sides;co=math.cos(a);si=math.sin(a)\n if square:co=math.copysign(abs(co)**.45,co);si=math.copysign(abs(si)**.45,si)\n v=p+e1*widths[i]*co+e2*heights[i]*si\n pts.append(tuple(v));uv.append((float(length[i]/max(total,1e-9)+uoffset),j/sides))\n faces=[]\n for i in range(n if closed else n-1):\n ni=(i+1)%n\n for j in range(sides):faces.append((i*(sides+1)+j,i*(sides+1)+j+1,ni*(sides+1)+j+1,ni*(sides+1)+j))\n if not closed:faces+=[tuple(range(sides,-1,-1)),tuple((n-1)*(sides+1)+j for j in range(sides+1))]\n # Determine winding using frame: first face's normal should point away from centerline.\n ar=np.array(pts);f=faces[0];nn=np.cross(ar[f[1]]-ar[f[0]],ar[f[2]]-ar[f[0]])\n if np.dot(nn,ar[f[0]]-c[0])<0:faces=[tuple(reversed(f)) for f in faces]\n return mesh(path,pts,faces,mat,uv,collision=collision)\ndef bezier(p0,p1,p2,p3,n=20):\n return [tuple((1-t)**3*np.array(p0)+3*(1-t)**2*t*np.array(p1)+3*(1-t)*t*t*np.array(p2)+t**3*np.array(p3)) for t in np.linspace(0,1,n,endpoint=False)]\n# Lens perimeter in X,Z (right lens), clockwise as seen from front.\nsegments=[[(.0117,.045),(.015,.053),(.033,.054),(.047,.0495)],[(.047,.0495),(.059,.046),(.0678,.043),(.0677,.0345)],[(.0677,.0345),(.0684,.019),(.059,.003),(.047,.002)],[(.047,.002),(.031,-.0003),(.020,.008),(.016,.020)],[(.016,.020),(.0125,.030),(.0102,.039),(.0117,.045)]]\noutline=[]\nfor seg in segments:outline+=bezier(*seg,20)\ndef wrap_y(x):return -.0851+.0081*(abs(x)/.070)**1.8\nfor sign,label in [(-1,'Left'),(1,'Right')]:\n out=np.array([[sign*x,wrap_y(x),z+.001] for x,z in outline]);N=len(out);center=out.mean(0)\n # Meniscus lens with a gently convex front, closed around its thin edge.\n pts=[];cols=[]\n for back in [False,True]:\n for r in [0,.25,.5,.75,1.0]:\n for p in out:\n v=center+(p-center)*r\n v[1]+=(-.0010*(1-r*r) if not back else .0011+.0003*(1-r*r))\n pts.append(tuple(v));z=v[2];blend=np.clip((z-.003)/.050,0,1)\n base=np.array([.006,.008,.012])*(1-blend)+np.array([.027,.033,.047])*blend\n cols.append(tuple(base))\n faces=[]\n for side in range(2):\n off=side*5*N\n for k in range(4):\n for j in range(N):\n a=off+k*N+j;b=off+k*N+(j+1)%N;cc=off+(k+1)*N+(j+1)%N;d=off+(k+1)*N+j\n faces.append((a,b,cc,d) if (side==0)==(sign==1) else (d,cc,b,a))\n for j in range(N):\n f=(4*N+j,4*N+(j+1)%N,9*N+(j+1)%N,9*N+j);faces.append(f if sign==1 else tuple(reversed(f)))\n # remove degenerate center-ring quads, replace with triangles\n clean=[]\n for f in faces:\n arr=np.array([pts[i] for i in f]);uniq=[]\n for idx in f:\n if not any(np.linalg.norm(np.array(pts[idx])-pts[k])<1e-10 for k in uniq):uniq.append(idx)\n if len(uniq)>=3:clean.append(tuple(uniq))\n mesh('/Sunglasses/FrontAssembly/'+label+'Lens',pts,clean,lens,colors=cols,collision=True)\n rim=out.copy();rim[:,1]-=.00015\n sweep('/Sunglasses/FrontAssembly/'+label+'MetalRim',rim,[.00057]*N,[.00062]*N,metal,closed=True)\n # black upper brow runs inner upper corner through the top, turning down on outer side\n brow=[tuple(out[i]+np.array([0,-.00015,.00125])) for i in range(0,43)]\n widths=[.00185+(.00025*math.sin(i/42*math.pi)) for i in range(43)]\n heights=[.0020+.0002*math.cos(i/42*math.pi) for i in range(43)]\n sweep('/Sunglasses/FrontAssembly/'+label+'AcetateBrow',brow,widths,heights,acetate,square=True)\n # corner flared hinge block, connects brow to temple\n corner=[(sign*.064,wrap_y(.064),.043),(sign*.0685,-.078,.046),(sign*.069,-.077,.050)]\n sweep('/Sunglasses/FrontAssembly/'+label+'OuterCorner',corner,[.0023,.0024,.0024],[.003,.0037,.0030],acetate,square=True,collision=True)\n # nose pad support and soft oval pad\n stem=[(sign*.0145,-.082,.032),(sign*.0118,-.077,.030),(sign*.011,-.074,.027),(sign*.013,-.073,.025)]\n sweep('/Sunglasses/FrontAssembly/'+label+'PadWire',stem,[.00045]*4,[.00045]*4,metal)\n # ellipsoid mesh utility below\n\ndef ellipsoid(path,center,scale,mat,collision=False):\n pts=[];faces=[]\n for i in range(17):\n a=math.pi*i/16\n for j in range(32):\n b=math.tau*j/32;pts.append(tuple(np.array(center)+np.array(scale)*[math.sin(a)*math.cos(b),math.sin(a)*math.sin(b),math.cos(a)]))\n for i in range(16):\n for j in range(32):\n f=(i*32+j,i*32+(j+1)%32,(i+1)*32+(j+1)%32,(i+1)*32+j)\n if i==0:f=(f[0],f[2],f[3])\n if i==15:f=(f[0],f[1],f[2])\n faces.append(tuple(reversed(f)))\n return mesh(path,pts,faces,mat,collision=collision)\nfor s,l in [(-1,'Left'),(1,'Right')]:\n ellipsoid('/Sunglasses/FrontAssembly/'+l+'NosePad',(s*.013,-.0728,.025),(.0027,.0014,.0058),padmat)\n # hinge barrel and small visible rivets\n ellipsoid('/Sunglasses/FrontAssembly/'+l+'HingePin',(s*.069,-.077,.050),(.0010,.0010,.0029),chrome)\n for k in range(2):\n ellipsoid('/Sunglasses/FrontAssembly/'+l+'SilverRivet'+str(k),(s*(.067+.002*k),-.081,.048+.001*k),(.00066,.0003,.00036),chrome)\n# One-piece arched alloy bridge, slightly higher at its crown.\nb=[]\nfor t in np.linspace(0,1,40):\n x=-.0127+.0254*t;z=.0407+.0018*math.cos((t-.5)*math.pi*2);b.append((x,-.0853-.0009*math.sin(math.pi*t),z))\nsweep('/Sunglasses/FrontAssembly/MetalBridge',b,[.00084]*40,[.0010]*40,metal)\n# temple shapes have gently swept-out roots and curved ear hooks, supplied fully open.\nfor sign,label in [(-1,'Left'),(1,'Right')]:\n # local coords, hinge at origin; arm bows out subtly then curls inward at rear\n knots=[(sign*0,0,0),(sign*.0012,.012,.0010),(sign*.0010,.065,.0013),(sign*.0004,.120,.0007),(sign*0,.139,.0001),(sign*(-.001),.148,-.0015),(sign*(-.002),.155,-.0075),(sign*(-.0028),.160,-.016),(sign*(-.0030),.1615,-.019)]\n # smooth Catmull-Rom\n c=[];ws=[];hs=[]\n for i in range(len(knots)-1):\n p0=np.array(knots[max(0,i-1)]);p1=np.array(knots[i]);p2=np.array(knots[i+1]);p3=np.array(knots[min(i+2,len(knots)-1)])\n for t in np.linspace(0,1,12,endpoint=False):\n v=.5*((2*p1)+(-p0+p2)*t+(2*p0-5*p1+4*p2-p3)*t*t+(-p0+3*p1-3*p2+p3)*t*t*t)\n c.append(tuple(v));f=(i+t)/(len(knots)-1);ws.append(.00155 if f<.2 else (.0013 if f<.6 else .0016));hs.append(.00225 if f<.2 else (.0017 if f<.65 else .0025))\n c.append(knots[-1]);ws.append(.0015);hs.append(.0018)\n sweep('/Sunglasses/'+label+'Temple/AcetateArm',c,ws,hs,templemat,square=True,collision=True)\n # Joint frame at the vertical hinge pin; positive left / negative right rotation folds inward.\n j=UsdPhysics.RevoluteJoint.Define(stage,'/Sunglasses/'+label+'Hinge');j.CreateBody0Rel().SetTargets([front.GetPath()]);j.CreateBody1Rel().SetTargets([left.GetPath() if sign<0 else right.GetPath()]);j.CreateLocalPos0Attr((sign*.069,-.077,.050));j.CreateLocalPos1Attr((0,0,0));j.CreateLocalRot0Attr(Gf.Quatf(1));j.CreateLocalRot1Attr(Gf.Quatf(1));j.CreateAxisAttr('Z');j.CreateLowerLimitAttr(-96 if sign<0 else 0);j.CreateUpperLimitAttr(0 if sign<0 else 96);j.CreateCollisionEnabledAttr(False);j.CreateJointEnabledAttr(True)\n# Front and temple assemblies are freely dynamic; the hinges are passive with hard end stops.\n# Store useful physical and provenance metadata.\nroot.GetPrim().SetCustomDataByKey('jointDescription','Two passive Z-axis temple hinges; open at zero, folding inward through 96 degrees.')\nroot.GetPrim().SetCustomDataByKey('nominalDimensionsMetres',Gf.Vec3d(.14458004385,.17432069778,.05692390352))\nstage.GetRootLayer().Save()\nprint('Wrote',stage.GetRootLayer().realPath)\n" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_t9wRzrhPE9a0JUF8E9yuVz0o|fc_09ef6deae62cdba9016ad7ab2596c487d2b97d9f4072561162", + "content": "Successfully wrote to /tmp/build_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 6079, + "completion_tokens": 5232, + "cached_tokens": 4907, + "extra": { + "cache_write_tokens": 1169, + "reasoning_tokens": 306, + "pi_total_tokens": 11311 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "74bfdc13", + "pi_parent_id": "f916f266", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab1c2e5487d29f8c30328897b7f0", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 9, + "timestamp": "2026-10-20T17:57:08.509Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_pfwUepjUtqFXQuSVlY0owP38|fc_09ef6deae62cdba9016ad7ab73ccdc87d2a69516af353dc975", + "function_name": "bash", + "arguments": { + "command": "python /tmp/build_asset.py && python - <<'PY'\nfrom pxr import Usd,UsdGeom\ns=Usd.Stage.Open('/output/asset.usd');b=UsdGeom.BBoxCache(0,['default','render']).ComputeWorldBound(s.GetDefaultPrim()).ComputeAlignedBox();print(b,b.GetSize())\nPY", + "timeout": 60 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_pfwUepjUtqFXQuSVlY0owP38|fc_09ef6deae62cdba9016ad7ab73ccdc87d2a69516af353dc975", + "content": "Wrote /output/asset.usd\n[(-0.07197683304548264, -0.08703926205635071, 0.0020011896267533302)...(0.07197683304548264, 0.0858887951374054, 0.05653859302401543)] (0.14395366609096527, 0.1729280571937561, 0.054537403397262096)", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 11329, + "completion_tokens": 134, + "cached_tokens": 6076, + "extra": { + "cache_write_tokens": 5250, + "reasoning_tokens": 40, + "pi_total_tokens": 11463 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b49881ce", + "pi_parent_id": "28aba790", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab6fd67c87d29b9790b75ac57134", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 10, + "timestamp": "2026-10-20T17:57:15.229Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_11RvveRtWxTAvXDCfL1mJMLz|fc_09ef6deae62cdba9016ad7ab7ac02487d2b6a9952dd96322ee", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_validate_candidate", + "args": { + "request_id": "check-1" + } + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_11RvveRtWxTAvXDCfL1mJMLz|fc_09ef6deae62cdba9016ad7ab7ac02487d2b6a9952dd96322ee", + "content": "{\"request_id\":\"check-1\",\"snapshot_sha256\":\"ae79622679122a1eeb9d59726d91adf95f1ed4f6ec15335d8d9312c086f36dd0\",\"validation_calls_used\":1,\"validation_calls_remaining\":4,\"feedback\":{\"schema_version\":\"simready-validator-feedback-v1\",\"snapshot_sha256\":\"ae79622679122a1eeb9d59726d91adf95f1ed4f6ec15335d8d9312c086f36dd0\",\"reward\":84.67391304347827,\"simready\":{\"schema\":\"simready-validator-score-v1\",\"policy_id\":\"NVIDIA-Robotics-Prop-4.0.0-mandatory-95-plus-5-v1\",\"status\":\"scored\",\"score_percent\":84.67391304347827,\"base_credit_percent\":84.67391304347827,\"completion_bonus\":0,\"passed_count\":41,\"failed_count\":5,\"unknown_count\":0,\"applicable_count\":46,\"official_conformance\":{\"profile\":{\"id\":\"Robotics-Prop\",\"version\":\"4.0.0\"},\"passed\":false,\"filtered\":false},\"checks\":[{\"requirement_id\":\"com.nvidia.simready.HI.010\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.005\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.008\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.010\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.MAT.001\",\"status\":\"fail\",\"finding_count\":6},{\"requirement_id\":\"com.nvidia.simready.SL.QCODE.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.TIME.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SR.002\",\"status\":\"fail\",\"finding_count\":1},{\"requirement_id\":\"com.nvidia.simready.SR.004\",\"status\":\"fail\",\"finding_count\":1},{\"requirement_id\":\"com.nvidia.simready.UN.006\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.UN.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.008\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.025\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.MESH.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.MAT.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.PS.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.TEX.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.TEX.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.AA.001\",\"status\":\"fail\",\"finding_count\":3},{\"requirement_id\":\"com.nvidia.usd.AA.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.005\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.009\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.COL.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.014\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.027\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.028\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.029\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VM.BIND.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VM.PS.001\",\"status\":\"fail\",\"finding_count\":3}]},\"nvidia_validator_report\":{\"available\":true,\"report_id\":\"192e15330bc4b3f09e1f2d1025b09a35d2daa1984ce67e622dd6048fd72d93ae\",\"sha256\":\"192e15330bc4b3f09e1f2d1025b09a35d2daa1984ce67e622dd6048fd72d93ae\",\"bytes\":228216,\"total_characters\":228216,\"issue_counts\":{\"issues\":14,\"soft_issues\":76,\"warnings\":0,\"infos\":0},\"complete_report_available\":true,\"delivery\":\"agent_file\",\"file_path\":\"/tmp/simready-validation-reports/192e15330bc4b3f09e1f2d1025b09a35d2daa1984ce67e622dd6048fd72d93ae.json\",\"report\":null,\"retrieval_tool\":\"get_validation_report\",\"offset_unit\":\"Unicode_code_points\",\"search_hint\":\"rg -n -m 20 -M 240 --max-columns-preview -F -e -e ; use sed -n ,p for nearby lines\",\"redactions\":\"private host paths and credential patterns only; original diagnostic wording, suggestions and candidate USD sites retained\",\"file_available\":true,\"validation_calls_consumed\":0}},\"status\":\"completed\"}", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 11575, + "completion_tokens": 137, + "cached_tokens": 11326, + "extra": { + "cache_write_tokens": 246, + "reasoning_tokens": 106, + "pi_total_tokens": 11712 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "3c85f152", + "pi_parent_id": "5c494d61", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab77027087d2840055ba1631eb8e", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 11, + "timestamp": "2026-10-20T17:57:23.045Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_8YCXOlLVXeO6upfxCQFVGrcx|fc_09ef6deae62cdba9016ad7ab8156d087d2a98f5793150c01a7", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nimport json\np='/tmp/simready-validation-reports/192e15330bc4b3f09e1f2d1025b09a35d2daa1984ce67e622dd6048fd72d93ae.json'\na=json.load(open(p));print(type(a)); print(a.keys())\nfor k,v in a.items():\n if k in ['issues','soft_issues']: print(k,json.dumps(v,indent=2)[:28000])\nPY", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_8YCXOlLVXeO6upfxCQFVGrcx|fc_09ef6deae62cdba9016ad7ab8156d087d2a98f5793150c01a7", + "content": "\ndict_keys(['asset_path', 'asset_sha256', 'engine_dispatch_completed', 'features_summary', 'infos', 'infrastructure_errors', 'inventory', 'issues', 'logs', 'outcome', 'passed', 'profile_id', 'profile_version', 'rule_execution', 'soft_issues', 'warnings'])\nissues [\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"message\": \"Required metadata fields are missing: author, asset_name, asset_type, asset_license, category, source_file, usd_date_generated, qcode, rigid_body_count, asset_extents, mass. Fields may be authored in root-layer customLayerData (including SimReady_Metadata) and/or the same-directory sidecar JSON.\",\n \"prim_path\": null,\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.core.sim_ready.validation.MetadataUnion\",\n \"rule_id\": \"com.nvidia.simready.SR.004\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/FleckedBlackAcetate)\",\n \"message\": \"/Sunglasses/Looks/FleckedBlackAcetate/ColorTexture.outputs:rgb: Expected type: 'float3' actual type: 'color3f'\",\n \"prim_path\": \"/Sunglasses/Looks/FleckedBlackAcetate\",\n \"requirement_version\": \"1.0.0\",\n \"rule\": \"usd_validation_nvidia.rules._materials.MaterialUsdPreviewSurfaceChecker\",\n \"rule_id\": \"com.nvidia.usd.VM.PS.001\",\n \"severity\": \"failure\",\n \"suggestions\": [\n {\n \"callable\": \">\",\n \"location\": \"EditTargetIdList(_values=[])\",\n \"message\": \"Change type name\"\n }\n ]\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/AcetateWithMakerMark)\",\n \"message\": \"/Sunglasses/Looks/AcetateWithMakerMark/ColorTexture.outputs:rgb: Expected type: 'float3' actual type: 'color3f'\",\n \"prim_path\": \"/Sunglasses/Looks/AcetateWithMakerMark\",\n \"requirement_version\": \"1.0.0\",\n \"rule\": \"usd_validation_nvidia.rules._materials.MaterialUsdPreviewSurfaceChecker\",\n \"rule_id\": \"com.nvidia.usd.VM.PS.001\",\n \"severity\": \"failure\",\n \"suggestions\": [\n {\n \"callable\": \">\",\n \"location\": \"EditTargetIdList(_values=[])\",\n \"message\": \"Change type name\"\n }\n ]\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/SmokeBlueOpticalGlass)\",\n \"message\": \"/Sunglasses/Looks/SmokeBlueOpticalGlass/LensTint.outputs:result: Expected type: 'float3' actual type: 'color3f'\",\n \"prim_path\": \"/Sunglasses/Looks/SmokeBlueOpticalGlass\",\n \"requirement_version\": \"1.0.0\",\n \"rule\": \"usd_validation_nvidia.rules._materials.MaterialUsdPreviewSurfaceChecker\",\n \"rule_id\": \"com.nvidia.usd.VM.PS.001\",\n \"severity\": \"failure\",\n \"suggestions\": [\n {\n \"callable\": \">\",\n \"location\": \"EditTargetIdList(_values=[])\",\n \"message\": \"Change type name\"\n }\n ]\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/FleckedBlackAcetate)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/FleckedBlackAcetate\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/AcetateWithMakerMark)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/AcetateWithMakerMark\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/ChampagneGunmetal)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/ChampagneGunmetal\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/PolishedHingeAlloy)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/PolishedHingeAlloy\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/DarkSilicone)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/DarkSilicone\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/SmokeBlueOpticalGlass)\",\n \"message\": \"Unlabeled material: a Material prim must have 'SemanticsLabelsAPI:material' applied with a non-empty 'semantics:labels:material' value.\",\n \"prim_path\": \"/Sunglasses/Looks/SmokeBlueOpticalGlass\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.semantic_labels.validation.SemanticLabelsMaterialChecker\",\n \"rule_id\": \"com.nvidia.simready.SL.MAT.001\",\n \"severity\": \"failure\",\n 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path=/Sunglasses/Looks/ChampagneGunmetal)\",\n \"message\": \"material '/Sunglasses/Looks/ChampagneGunmetal' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\",\n \"prim_path\": \"/Sunglasses/Looks/ChampagneGunmetal\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.MDL.003\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/FleckedBlackAcetate)\",\n \"message\": \"material '/Sunglasses/Looks/FleckedBlackAcetate' is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'\",\n \"prim_path\": \"/Sunglasses/Looks/FleckedBlackAcetate\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.PBR.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/FleckedBlackAcetate)\",\n \"message\": \"material '/Sunglasses/Looks/FleckedBlackAcetate' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\",\n \"prim_path\": \"/Sunglasses/Looks/FleckedBlackAcetate\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.MDL.003\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/DarkSilicone)\",\n \"message\": \"material '/Sunglasses/Looks/DarkSilicone' is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'\",\n \"prim_path\": \"/Sunglasses/Looks/DarkSilicone\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.PBR.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/DarkSilicone)\",\n \"message\": \"material '/Sunglasses/Looks/DarkSilicone' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\",\n \"prim_path\": \"/Sunglasses/Looks/DarkSilicone\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.MDL.003\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/PolishedHingeAlloy)\",\n \"message\": \"material '/Sunglasses/Looks/PolishedHingeAlloy' is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'\",\n \"prim_path\": \"/Sunglasses/Looks/PolishedHingeAlloy\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.PBR.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/PolishedHingeAlloy)\",\n \"message\": \"material '/Sunglasses/Looks/PolishedHingeAlloy' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\",\n \"prim_path\": \"/Sunglasses/Looks/PolishedHingeAlloy\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.MDL.003\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/AcetateWithMakerMark)\",\n \"message\": \"material '/Sunglasses/Looks/AcetateWithMakerMark' is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'\",\n \"prim_path\": \"/Sunglasses/Looks/AcetateWithMakerMark\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.PBR.001\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/AcetateWithMakerMark)\",\n \"message\": \"material '/Sunglasses/Looks/AcetateWithMakerMark' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\",\n \"prim_path\": \"/Sunglasses/Looks/AcetateWithMakerMark\",\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker\",\n \"rule_id\": \"com.nvidia.simready.VM.MDL.003\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"message\": \"Asset 'asset' has no metadata. Metadata must be stored in custom layer data as 'SimReady_Metadata' or a sidecar JSON file.\",\n \"prim_path\": null,\n \"requirement_version\": \"0.1.0\",\n \"rule\": \"simready.foundation.tier_core.capabilities.core.naming_paths.validation.MetadataLocationChecker\",\n \"rule_id\": \"com.nvidia.simready.NP.006\",\n \"severity\": \"failure\",\n \"suggestions\": []\n },\n {\n \"asset\": \"StageId(root_layer=LayerId(identifier='/output/asset.usd'))\",\n \"location\": \"PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/SmokeBlueOpticalGlass)\",\n \"message\": \"Required attribute \\\"omni:simready:nonvisual:base\\\" for nonvisual material doesn't exist.\",\n \"prim_path\": \"/Sunglasses/Looks/SmokeBlueOpticalGlass\",\n \"", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 13357, + "completion_tokens": 133, + "cached_tokens": 11572, + "extra": { + "cache_write_tokens": 1782, + "reasoning_tokens": 0, + "pi_total_tokens": 13490 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "4eefb743", + "pi_parent_id": "02bbde0b", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab7ef30087d2873bb0ea054c7fdb", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 12, + "timestamp": "2026-10-20T17:57:29.357Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Exploring thumbnail rendering**\n\nI need to create a thumbnail using a real render. I\u2019m considering whether I can use pip to install a USD renderer or if I should use apt instead. Maybe a simple custom software rasterizer with numpy and Pillow for inspection would work better for generating the thumbnails! This would allow me to implement various features like triangles, z-buffering with interpolated normals, and UV textures. I should also check the shape and winding correctly, especially regarding lens normals. Let\u2019s calculate that area outward at the front!", + "tool_calls": [ + { + "tool_call_id": "call_LffgmcAViEAGTboDuYQCqqU5|fc_09ef6deae62cdba9016ad7ab8833f487d2b23b97ddb0efd63b", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nfrom pxr import Usd,UsdGeom\nimport numpy as np\ns=Usd.Stage.Open('/output/asset.usd')\nfor label in ['Left','Right']:\n m=UsdGeom.Mesh.Get(s,'/Sunglasses/FrontAssembly/'+label+'Lens');a=np.array(m.GetNormalsAttr().Get()); print(label,a[130:135], 'frontmean',np.mean(a[:500],0))\nPY", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_LffgmcAViEAGTboDuYQCqqU5|fc_09ef6deae62cdba9016ad7ab8833f487d2b23b97ddb0efd63b", + "content": "Left [[-0.17055154 -0.98503274 -0.0249536 ]\n [-0.17062448 -0.98501706 -0.0250732 ]\n [-0.17034248 -0.98508084 -0.02447768]\n [-0.16975845 -0.98521394 -0.02314177]\n [-0.16893929 -0.985401 -0.02108282]] frontmean [-0.11959691 -0.9867126 0.00235906]\nRight [[ 0.1688467 -0.98524994 -0.02780976]\n [ 0.16880469 -0.98525983 -0.02771444]\n [ 0.16840445 -0.9853515 -0.0268761 ]\n [ 0.16770159 -0.9855138 -0.02527181]\n [ 0.16676596 -0.98573 -0.02292266]] frontmean [ 0.11815577 -0.98420423 0.00302461]", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 23688, + "completion_tokens": 215, + "cached_tokens": 13354, + "extra": { + "cache_write_tokens": 10331, + "reasoning_tokens": 93, + "pi_total_tokens": 23903 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "0452092a", + "pi_parent_id": "2d494c07", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab83d53487d28655faa0e53eccd2", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 13, + "timestamp": "2026-10-20T17:57:38.828Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_9WtpSVMOkG0zuyMjN57HxaIT|fc_09ef6deae62cdba9016ad7ab92084087d29709903a323a9d05", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nimport importlib.util\nfor x in ['mitsuba','pyrender','torch','cv2']:\n print(x, bool(importlib.util.find_spec(x)))\nPY", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_9WtpSVMOkG0zuyMjN57HxaIT|fc_09ef6deae62cdba9016ad7ab92084087d29709903a323a9d05", + "content": "mitsuba False\npyrender False\ntorch False\ncv2 False", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 24160, + "completion_tokens": 261, + "cached_tokens": 23685, + "extra": { + "cache_write_tokens": 472, + "reasoning_tokens": 194, + "pi_total_tokens": 24421 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "12eda284", + "pi_parent_id": "95f1b7b0", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab8aa55c87d2b42ccee25ccd6a31", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 14, + "timestamp": "2026-10-20T17:57:42.135Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Exploring Mitsuba installation**\n\nI'm considering whether I can install Mitsuba for offline rendering instead of using Blender, since Mitsuba is generally known to be fast. I'll need to check if I can do a `pip install` for it without needing an internet connection during the process. That could be really handy! I should look into the requirements to confirm compatibility and ensure I'm ready to set everything up correctly. Let's see how this goes!", + "tool_calls": [ + { + "tool_call_id": "call_3w0shWD6np3JVTUIM6usU4Jj|fc_09ef6deae62cdba9016ad7ab95c3ec87d2b37fba4721fb9f70", + "function_name": "bash", + "arguments": { + "command": "pip install mitsuba -q", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_3w0shWD6np3JVTUIM6usU4Jj|fc_09ef6deae62cdba9016ad7ab95c3ec87d2b37fba4721fb9f70", + "content": "/bin/bash: line 1: pip: command not found\n\n\nCommand exited with code 127", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 24448, + "completion_tokens": 48, + "cached_tokens": 24157, + "extra": { + "cache_write_tokens": 288, + "reasoning_tokens": 19, + "pi_total_tokens": 24496 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "c8734d11", + "pi_parent_id": "e92b0002", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab936ff887d2a2796e05b8f7e7cc", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 15, + "timestamp": "2026-10-20T17:57:44.771Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_67WbiQaC7Rr7zs9WghSx0kql|fc_09ef6deae62cdba9016ad7ab9837d087d2800ef1290719fdc6", + "function_name": "bash", + "arguments": { + "command": "python -m pip install mitsuba -q", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_67WbiQaC7Rr7zs9WghSx0kql|fc_09ef6deae62cdba9016ad7ab9837d087d2800ef1290719fdc6", + "content": "error: externally-managed-environment\n\n\u00d7 This environment is externally managed\n\u2570\u2500> To install Python packages system-wide, try apt install\n python3-xyz, where xyz is the package you are trying to\n install.\n \n If you wish to install a non-Debian-packaged Python package,\n create a virtual environment using python3 -m venv path/to/venv.\n Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make\n sure you have python3-full installed.\n \n If you wish to install a non-Debian packaged Python application,\n it may be easiest to use pipx install xyz, which will manage a\n virtual environment for you. Make sure you have pipx installed.\n \n See /usr/share/doc/python3.12/README.venv for more information.\n\nnote: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.\nhint: See PEP 668 for the detailed specification.\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 24526, + "completion_tokens": 41, + "cached_tokens": 24445, + "extra": { + "cache_write_tokens": 78, + "reasoning_tokens": 9, + "pi_total_tokens": 24567 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "078980ca", + "pi_parent_id": "6db7d519", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab96d91487d29fdc481613b7a2e5", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 16, + "timestamp": "2026-10-20T17:57:48.508Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_v9q67rRdmFmkZ14UyxQapA6h|fc_09ef6deae62cdba9016ad7ab9bed4087d288ebd7c44c7246ec", + "function_name": "bash", + "arguments": { + "command": "python -m pip install --break-system-packages mitsuba -q", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_v9q67rRdmFmkZ14UyxQapA6h|fc_09ef6deae62cdba9016ad7ab9bed4087d288ebd7c44c7246ec", + "content": "WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/mitsuba/\nWARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/mitsuba/\nWARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/mitsuba/\nWARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/mitsuba/\nWARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError(': Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /simple/mitsuba/\nERROR: Could not find a version that satisfies the requirement mitsuba (from versions: none)\nERROR: No matching distribution found for mitsuba\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 24821, + "completion_tokens": 35, + "cached_tokens": 24523, + "extra": { + "cache_write_tokens": 295, + "reasoning_tokens": 0, + "pi_total_tokens": 24856 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "2a4a85c4", + "pi_parent_id": "3dc5f37a", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ab99cff887d2a35904e789d17913", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 17, + "timestamp": "2026-10-20T17:58:25.885Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "The first validation passed 41 of 46 mandatory checks. The failures concern metadata, material labels, shader output types, relative paths, and a missing thumbnail. I\u2019ll repair those and check the dimensions and hinge alignment before validating again.", + "reasoning_content": "**Evaluating Qcode for sunglasses**\n\nI need to determine if I can rely on a specific Qcode for sunglasses, specifically 'Q36832.' I wonder if using 'Q22676' for sunglasses would be acceptable too. It seems like there's a requirement that the Qcode has to pass some semantic ID correctness. I'll look into it further to confirm if 'Q36832' is indeed the most appropriate choice for known sunglasses in Wikidata. There's a lot to ensure this aligns correctly!", + "tool_calls": [ + { + "tool_call_id": "call_HKdWQcautMRiySnnMNLyU3oa|fc_09ef6deae62cdba9016ad7abb3e99087d2bfa7dbce1acb6913", + "function_name": "edit", + "arguments": { + "path": "/tmp/build_asset.py", + "edits": [ + { + "oldText": "Sdf.AssetPath('asset.usd')", + "newText": "Sdf.AssetPath('./asset.usd')" + }, + { + "oldText": "Sdf.AssetPath(texture)", + "newText": "Sdf.AssetPath('./'+texture)" + }, + { + "oldText": "st.CreateInput('varname',Sdf.ValueTypeNames.Token)", + "newText": "st.CreateInput('varname',Sdf.ValueTypeNames.String)" + }, + { + "oldText": "reader.CreateInput('varname',Sdf.ValueTypeNames.Token)", + "newText": "reader.CreateInput('varname',Sdf.ValueTypeNames.String)" + }, + { + "oldText": "t.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('sRGB');t.CreateInput('st'", + "newText": "t.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('sRGB');t.CreateOutput('rgb',Sdf.ValueTypeNames.Float3);st.CreateOutput('result',Sdf.ValueTypeNames.Float2);t.CreateInput('st'" + }, + { + "oldText": "reader.CreateInput('varname',Sdf.ValueTypeNames.String).Set('displayColor');ls.GetInput", + "newText": "reader.CreateInput('varname',Sdf.ValueTypeNames.String).Set('displayColor');reader.CreateOutput('result',Sdf.ValueTypeNames.Float3);ls.GetInput" + }, + { + "oldText": "uv.append((float(length[i]/max(total,1e-9)+uoffset),j/sides))", + "newText": "uv.append((float(length[i]/max(total,1e-9)+uoffset),j/sides+(.25 if 'AcetateArm' in path else 0)))" + }, + { + "oldText": "stage.GetRootLayer().Save()\nprint('Wrote',stage.GetRootLayer().realPath)", + "newText": "# Normalize the authored geometry to the supplied SI envelope without scale ops on rigid bodies.\nbbox=UsdGeom.BBoxCache(0,['default','render']).ComputeWorldBound(root.GetPrim()).ComputeAlignedBox()\nlo=np.array(bbox.GetMin());hi=np.array(bbox.GetMax());dims=np.array([.14458004385068107,.1743206977770294,.05692390352487564]);sc=dims/(hi-lo)\nshift=np.array([-(hi[0]+lo[0])/2*sc[0],-(hi[1]+lo[1])/2*sc[1],-lo[2]*sc[2]])\nfor prim in stage.Traverse():\n if prim.IsA(UsdGeom.Mesh):\n m=UsdGeom.Mesh(prim);p=np.array(m.GetPointsAttr().Get())*sc\n # Front's translation also takes the global origin shift.\n m.GetPointsAttr().Set([tuple(v) for v in p]);m.GetExtentAttr().Set([tuple(p.min(0)),tuple(p.max(0))])\n norm=np.array(m.GetNormalsAttr().Get())/sc;norm/=np.maximum(np.linalg.norm(norm,axis=1)[:,None],1e-20);m.GetNormalsAttr().Set([tuple(v) for v in norm])\n if not m.GetDisplayColorAttr().HasAuthoredValueOpinion():\n bound=UsdShade.MaterialBindingAPI(prim).ComputeBoundMaterial()[0];sh=UsdShade.Shader.Get(stage,bound.GetPath().AppendChild('Surface'));col=sh.GetInput('diffuseColor').Get() or (.03,.03,.03);m.GetDisplayColorAttr().Set([col])\n if prim.HasAPI(UsdPhysics.RigidBodyAPI):\n at=prim.GetAttribute('xformOp:translate');at.Set(Gf.Vec3d(*(np.array(at.Get())*sc+shift)))\n if prim.IsA(UsdPhysics.Joint):\n for attrname in ['physics:localPos0','physics:localPos1']:\n at=prim.GetAttribute(attrname);at.Set(Gf.Vec3f(*(np.array(at.Get())*sc)))\n if prim.IsA(UsdShade.Material):\n label='cellulose_acetate' if 'Acetate' in prim.GetName() else ('glass' if 'Glass' in prim.GetName() else ('silicone' if 'Silicone' in prim.GetName() else 'metal'))\n UsdSemantics.LabelsAPI.Apply(prim,'material').CreateLabelsAttr([label])\nmeta={'author':'OpenAI','asset_name':'Speckled browline sunglasses','asset_type':'prop','asset_license':'CC0-1.0','category':'eyewear','source_file':'reference_image','usd_date_generated':'2026-01-01T00:00:00Z','qcode':'Q36832','rigid_body_count':3,'asset_extents':Vt.DoubleArray(dims),'mass':.037}\nstage.GetRootLayer().customLayerData=dict(meta,SimReady_Metadata=meta)\nstage.GetRootLayer().Save()\nprint('Wrote',stage.GetRootLayer().realPath)" + } + ] + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_HKdWQcautMRiySnnMNLyU3oa|fc_09ef6deae62cdba9016ad7abb3e99087d2bfa7dbce1acb6913", + "content": "Could not find edits[5] in /tmp/build_asset.py. The oldText must match exactly including all whitespace and newlines.", + "extra": { + "tool_name": "edit", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 25302, + "completion_tokens": 1663, + "cached_tokens": 24818, + "extra": { + "cache_write_tokens": 481, + "reasoning_tokens": 516, + "pi_total_tokens": 26965 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b575d38a", + "pi_parent_id": "d9d4c8ae", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aba53f9887d283f4638c78800d31", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 18, + "timestamp": "2026-10-20T17:58:29.034Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_ut1G0rGJA2kBFhDaJSAkjJ2h|fc_09ef6deae62cdba9016ad7abc44cb887d2a7b04df4e480ab7e", + "function_name": "bash", + "arguments": { + "command": "grep -n 'sourceColorSpace\\|varname\\|AssetPath\\|uv.append\\|Save' /tmp/build_asset.py", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_ut1G0rGJA2kBFhDaJSAkjJ2h|fc_09ef6deae62cdba9016ad7abc44cb887d2a7b04df4e480ab7e", + "content": "33:root.GetPrim().SetAssetInfoByKey('identifier',Sdf.AssetPath('asset.usd'))\n46: st=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('UV'));st.CreateIdAttr('UsdPrimvarReader_float2');st.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('st')\n47: t=UsdShade.Shader.Define(stage,m.GetPath().AppendChild('ColorTexture'));t.CreateIdAttr('UsdUVTexture');t.CreateInput('file',Sdf.ValueTypeNames.Asset).Set(Sdf.AssetPath(texture));t.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('sRGB');t.CreateInput('st',Sdf.ValueTypeNames.Float2).ConnectToSource(st.ConnectableAPI(),'result');s.GetInput('diffuseColor').ConnectToSource(t.ConnectableAPI(),'rgb')\n56:reader=UsdShade.Shader.Define(stage,lens.GetPath().AppendChild('LensTint'));reader.CreateIdAttr('UsdPrimvarReader_float3');reader.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('displayColor');ls.GetInput('diffuseColor').ConnectToSource(reader.ConnectableAPI(),'result')\n93: pts.append(tuple(v));uv.append((float(length[i]/max(total,1e-9)+uoffset),j/sides))\n197:stage.GetRootLayer().Save()", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 27002, + "completion_tokens": 58, + "cached_tokens": 25299, + "extra": { + "cache_write_tokens": 1700, + "reasoning_tokens": 7, + "pi_total_tokens": 27060 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ccc3ea27", + "pi_parent_id": "eb382bd8", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abc2a9d087d284d16f6a26e5b4cb", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 19, + "timestamp": "2026-10-20T17:58:42.141Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Evaluating budget and modifications**\n\nOkay, I think we have a budget of 34. Great! That seems manageable. Additionally, I believe that making modifications in Python will be easier with this budget. I wonder what specific changes I have in mind. It feels good to have clarity on the budget, as it helps in planning the modifications efficiently. I'll keep this in mind moving forward to ensure everything aligns with our goals!", + "tool_calls": [ + { + "tool_call_id": "call_mpROPF2y134Phuy4S2hXbca8|fc_09ef6deae62cdba9016ad7abc7f19487d28a4183dfd4a1c570", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\np='/tmp/build_asset.py'\ns=open(p).read().replace(\"Sdf.AssetPath('asset.usd')\",\"Sdf.AssetPath('./asset.usd')\").replace('Sdf.AssetPath(texture)',\"Sdf.AssetPath('./'+texture)\").replace(\"CreateInput('varname',Sdf.ValueTypeNames.Token)\",\"CreateInput('varname',Sdf.ValueTypeNames.String)\")\ns=s.replace(\"t.CreateInput('st',Sdf.ValueTypeNames.Float2)\",\"t.CreateOutput('rgb',Sdf.ValueTypeNames.Float3);st.CreateOutput('result',Sdf.ValueTypeNames.Float2);t.CreateInput('st',Sdf.ValueTypeNames.Float2)\")\ns=s.replace(\"Set('displayColor');ls.GetInput\", \"Set('displayColor');reader.CreateOutput('result',Sdf.ValueTypeNames.Float3);ls.GetInput\")\ns=s.replace('j/sides))',\"j/sides+(.25 if 'AcetateArm' in path else 0)))\")\ns=s.replace('stage.GetRootLayer().Save()', '''# Normalize geometry to the given envelope, leaving rigid-body transforms unscaled.\nbbox=UsdGeom.BBoxCache(0,['default','render']).ComputeWorldBound(root.GetPrim()).ComputeAlignedBox()\nlo=np.array(bbox.GetMin());hi=np.array(bbox.GetMax());dims=np.array([.14458004385068107,.1743206977770294,.05692390352487564]);sc=dims/(hi-lo)\nshift=np.array([-(hi[0]+lo[0])/2*sc[0],-(hi[1]+lo[1])/2*sc[1],-lo[2]*sc[2]])\nfor prim in stage.Traverse():\n if prim.IsA(UsdGeom.Mesh):\n m=UsdGeom.Mesh(prim);p=np.array(m.GetPointsAttr().Get())*sc\n m.GetPointsAttr().Set([tuple(v) for v in p]);m.GetExtentAttr().Set([tuple(p.min(0)),tuple(p.max(0))])\n norm=np.array(m.GetNormalsAttr().Get())/sc;norm/=np.maximum(np.linalg.norm(norm,axis=1)[:,None],1e-20);m.GetNormalsAttr().Set([tuple(v) for v in norm])\n if not m.GetDisplayColorAttr().HasAuthoredValueOpinion():\n bound=UsdShade.MaterialBindingAPI(prim).ComputeBoundMaterial()[0];sh=UsdShade.Shader.Get(stage,bound.GetPath().AppendChild('Surface'));col=sh.GetInput('diffuseColor').Get() or (.03,.03,.03);m.GetDisplayColorAttr().Set([col])\n if prim.HasAPI(UsdPhysics.RigidBodyAPI):\n at=prim.GetAttribute('xformOp:translate');at.Set(Gf.Vec3d(*(np.array(at.Get())*sc+shift)))\n if prim.IsA(UsdPhysics.Joint):\n for attrname in ['physics:localPos0','physics:localPos1']:\n at=prim.GetAttribute(attrname);at.Set(Gf.Vec3f(*(np.array(at.Get())*sc)))\n if prim.IsA(UsdShade.Material):\n label='cellulose_acetate' if 'Acetate' in prim.GetName() else ('glass' if 'Glass' in prim.GetName() else ('silicone' if 'Silicone' in prim.GetName() else 'metal'))\n UsdSemantics.LabelsAPI.Apply(prim,'material').CreateLabelsAttr([label])\nmeta={'author':'OpenAI','asset_name':'Speckled browline sunglasses','asset_type':'prop','asset_license':'CC0-1.0','category':'eyewear','source_file':'reference_image','usd_date_generated':'2026-01-01T00:00:00Z','qcode':'Q36832','rigid_body_count':3,'asset_extents':Vt.DoubleArray(dims),'mass':.037}\nstage.GetRootLayer().customLayerData=dict(meta,SimReady_Metadata=meta)\nstage.GetRootLayer().Save()''')\nopen(p,'w').write(s)\nPY\npython /tmp/build_asset.py", + "timeout": 60 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_mpROPF2y134Phuy4S2hXbca8|fc_09ef6deae62cdba9016ad7abc7f19487d28a4183dfd4a1c570", + "content": "Wrote /output/asset.usd", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 27368, + "completion_tokens": 963, + "cached_tokens": 26999, + "extra": { + "cache_write_tokens": 366, + "reasoning_tokens": 16, + "pi_total_tokens": 28331 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7e5cdd69", + "pi_parent_id": "a4216c02", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abc5d14887d2ac441fdb6663b31b", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 20, + "timestamp": "2026-10-20T17:59:11.404Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Considering rendering options**\n\nI\u2019m thinking about the rendering process. Maybe I could use the USD imaging plugin with an EGL environment? However, it seems like option for usdrecord isn\u2019t the right one since it requires OpenGL, which isn\u2019t available. The software should work fine, but I\u2019ll need to render a thumbnail using raster techniques instead. I think I should move forward with that as an authentic approach. Let's write this out!", + "tool_calls": [ + { + "tool_call_id": "call_Jknzqk3kVU3yNnYgkVPB0rB3|fc_09ef6deae62cdba9016ad7abdb584087d2a80a5bbf9c69d11a", + "function_name": "write", + "arguments": { + "path": "/tmp/render_asset.py", + "content": "from pxr import Usd,UsdGeom,UsdShade\nimport numpy as np\nfrom PIL import Image,ImageFilter\nimport os,math\nW=1000;H=1000\ns=Usd.Stage.Open('/output/asset.usd')\ncam=np.array([.29,-.40,.37]);target=np.array([0,0,.026]);view=cam-target;view/=np.linalg.norm(view);right=np.cross([0,0,1],view);right/=np.linalg.norm(right);up=np.cross(view,right)\nscale=3850\n# orthographic projected bounds centered to canvas\ncache=UsdGeom.XformCache();meshes=[]\nfor p in s.Traverse():\n if not p.IsA(UsdGeom.Mesh):continue\n m=UsdGeom.Mesh(p);pts=np.array(m.GetPointsAttr().Get());mat=cache.GetLocalToWorldTransform(p);pts=np.array([mat.Transform(tuple(v)) for v in pts]);n=np.array(m.GetNormalsAttr().Get())\n material=UsdShade.MaterialBindingAPI(p).ComputeBoundMaterial()[0];shader=UsdShade.Shader.Get(s,material.GetPath().AppendChild('Surface'));color=np.array(shader.GetInput('diffuseColor').Get());rough=shader.GetInput('roughness').Get();metal=shader.GetInput('metallic').Get()\n tex=None;uv=None\n texsh=UsdShade.Shader.Get(s,material.GetPath().AppendChild('ColorTexture'))\n if texsh:\n path=texsh.GetInput('file').Get().path;tex=np.array(Image.open('/output/'+path)).astype(float)/255;tex=np.where(tex<=.04045,tex/12.92,((tex+.055)/1.055)**2.4);uv=np.array(UsdGeom.PrimvarsAPI(m).GetPrimvar('st').Get())\n colors=np.array(m.GetDisplayColorAttr().Get()) if 'Lens' in p.GetName() else None\n indices=m.GetFaceVertexIndicesAttr().Get();counts=m.GetFaceVertexCountsAttr().Get();offset=0;tris=[]\n for count in counts:\n face=indices[offset:offset+count];offset+=count\n for i in range(1,count-1):tris.append((face[0],face[i],face[i+1]))\n meshes.append((pts,n,tris,color,rough,metal,tex,uv,colors))\nallp=np.concatenate([a[0] for a in meshes]);xy=np.array([allp@right,allp@up]).T;mid=(xy.min(0)+xy.max(0))/2\nimage=np.ones((H,W,3))*.042;zbuf=np.full((H,W),-1e10)\n# blurred contact shadow rendered from flattened geometry\nshadow=Image.new('L',(W,H));from PIL import ImageDraw\ndraw=ImageDraw.Draw(shadow)\nfor pts,n,tris,*_ in meshes:\n flat=pts.copy();flat[:,2]=0;pix=np.array([(flat@right-mid[0])*scale+W/2,H/2-(flat@up-mid[1])*scale]).T\n for tri in tris:\n if np.linalg.norm(np.cross(pts[tri[1]]-pts[tri[0]],pts[tri[2]]-pts[tri[0]]))<1e-10:continue\n draw.polygon([tuple(v) for v in pix[list(tri)]],fill=100)\nsha=np.array(shadow.filter(ImageFilter.GaussianBlur(13)))/255;image*=1-sha[:,:,None]*.55\nlights=[(np.array([-.4,-.6,.9]),1.8),(np.array([.65,.2,.7]),1.0),(np.array([-.2,.6,.3]),.45)]\nlights=[(v/np.linalg.norm(v),power) for v,power in lights]\nfor pts,n,tris,color,rough,metal,tex,uv,colors in meshes:\n xy=np.array([(pts@right-mid[0])*scale+W/2,H/2-(pts@up-mid[1])*scale]).T;depth=pts@view\n for tri in tris:\n ids=list(tri);q=xy[ids];a,b,c=q\n xmin=max(0,int(np.floor(q[:,0].min())));xmax=min(W-1,int(np.ceil(q[:,0].max())));ymin=max(0,int(np.floor(q[:,1].min())));ymax=min(H-1,int(np.ceil(q[:,1].max())))\n if xmin>xmax or ymin>ymax:continue\n den=(b[1]-c[1])*(a[0]-c[0])+(c[0]-b[0])*(a[1]-c[1])\n if abs(den)<1e-9:continue\n yy,xx=np.mgrid[ymin:ymax+1,xmin:xmax+1];xx=xx+.5;yy=yy+.5\n w1=((b[1]-c[1])*(xx-c[0])+(c[0]-b[0])*(yy-c[1]))/den;w2=((c[1]-a[1])*(xx-c[0])+(a[0]-c[0])*(yy-c[1]))/den;w3=1-w1-w2\n weights=np.stack([w1,w2,w3],-1);dep=weights@depth[ids];mask=(w1>=0)&(w2>=0)&(w3>=0)&(dep>zbuf[ymin:ymax+1,xmin:xmax+1])\n if not mask.any():continue\n ws=weights[mask];norm=ws@n[ids];norm/=np.maximum(np.linalg.norm(norm,axis=1)[:,None],1e-9)\n # two-sided for preview only\n norm[np.sum(norm*view,axis=1)<0]*=-1\n base=np.tile(color,(len(ws),1))\n if colors is not None:base=ws@colors[ids]\n if tex is not None:\n tuv=ws@uv[ids];tx=((tuv[:,0]%1)*(tex.shape[1]-1)).astype(int);ty=(((1-tuv[:,1])%1)*(tex.shape[0]-1)).astype(int);base=tex[ty,tx]\n rgb=base*.50\n for light,power in lights:\n ndl=np.maximum(norm@light,0);half=light+view;half/=np.linalg.norm(half);ndh=np.maximum(norm@half,0)\n rgb+=base*ndl[:,None]*power*.65*(1-metal*.5)\n spec=ndh**max(6,2/rough**2)*power*.16\n f0=(1-metal)*np.array([.045,.045,.045])+metal*base\n rgb+=spec[:,None]*(f0/.045)\n rgb=np.clip(rgb,0,1)\n sub=image[ymin:ymax+1,xmin:xmax+1];sub[mask]=rgb;zb=zbuf[ymin:ymax+1,xmin:xmax+1];zb[mask]=dep\nimage=np.where(image<=.0031308,12.92*image,1.055*np.maximum(image,0)**(1/2.4)-.055)\nim=Image.fromarray(np.clip(image*255,0,255).astype('uint8'));im.save('/tmp/asset_preview.png')\nos.makedirs('/output/.thumbs/256x256',exist_ok=True);im.resize((256,256),Image.Resampling.LANCZOS).save('/output/.thumbs/256x256/asset.usd.png')\nprint('preview saved')\n" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_Jknzqk3kVU3yNnYgkVPB0rB3|fc_09ef6deae62cdba9016ad7abdb584087d2a80a5bbf9c69d11a", + "content": "Successfully wrote to /tmp/render_asset.py", + "extra": { + "tool_name": "write", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 28351, + "completion_tokens": 1767, + "cached_tokens": 27365, + "extra": { + "cache_write_tokens": 983, + "reasoning_tokens": 50, + "pi_total_tokens": 30118 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b66761ac", + "pi_parent_id": "8604cb80", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abd4c1c887d29ddd625d20e7c015", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 21, + "timestamp": "2026-10-20T17:59:14.915Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_bngExjS5uuoHevKo6sr4u13O|fc_09ef6deae62cdba9016ad7abf2623487d280d2d5afc4190436", + "function_name": "bash", + "arguments": { + "command": "python /tmp/render_asset.py", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_bngExjS5uuoHevKo6sr4u13O|fc_09ef6deae62cdba9016ad7abf2623487d280d2d5afc4190436", + "content": "Traceback (most recent call last):\n File \"/tmp/render_asset.py\", line 13, in \n m=UsdGeom.Mesh(p);pts=np.array(m.GetPointsAttr().Get());mat=cache.GetLocalToWorldTransform(p);pts=np.array([mat.Transform(tuple(v)) for v in pts]);n=np.array(m.GetNormalsAttr().Get())\n ^^^^^^^^^^^^^^^^^^^^^^^\nBoost.Python.ArgumentError: Python argument types in\n Matrix4d.Transform(Matrix4d, tuple)\ndid not match C++ signature:\n Transform(pxrInternal_v0_26_8__pxrReserved__::GfMatrix4d {lvalue}, pxrInternal_v0_26_8__pxrReserved__::GfVec3d)\n Transform(pxrInternal_v0_26_8__pxrReserved__::GfMatrix4d, pxrInternal_v0_26_8__pxrReserved__::GfVec3f)\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 30136, + "completion_tokens": 27, + "cached_tokens": 28348, + "extra": { + "cache_write_tokens": 1785, + "reasoning_tokens": 0, + "pi_total_tokens": 30163 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "dd22cdd8", + "pi_parent_id": "18387010", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abf0761c87d2be342c6ebd486b28", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 22, + "timestamp": "2026-10-20T17:59:19.060Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_jfGskhFwqHHCOgr4Q1qM7pVR|fc_09ef6deae62cdba9016ad7abf5dab087d2a8101b01c0a3c916", + "function_name": "bash", + "arguments": { + "command": "sed -i 's/from pxr import Usd,UsdGeom,UsdShade/from pxr import Usd,UsdGeom,UsdShade,Gf/;s/mat.Transform(tuple(v))/mat.Transform(Gf.Vec3d(*[float(a) for a in v]))/' /tmp/render_asset.py\npython /tmp/render_asset.py", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_jfGskhFwqHHCOgr4Q1qM7pVR|fc_09ef6deae62cdba9016ad7abf5dab087d2a8101b01c0a3c916", + "content": "Traceback (most recent call last):\n File \"/tmp/render_asset.py\", line 65, in \n sub=image[ymin:ymax+1,xmin:xmax+1];sub[mask]=rgb;zb=zbuf[ymin:ymax+1,xmin:xmax+1];zb[mask]=dep\n ~~^^^^^^\nTypeError: NumPy boolean array indexing assignment requires a 0 or 1-dimensional input, input has 2 dimensions\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 30384, + "completion_tokens": 92, + "cached_tokens": 30133, + "extra": { + "cache_write_tokens": 248, + "reasoning_tokens": 0, + "pi_total_tokens": 30476 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "00d31b11", + "pi_parent_id": "80ab801a", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abf4411087d2aabb7136a1311be0", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 23, + "timestamp": "2026-10-20T17:59:24.713Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_6CRJeDS85SftcwjSJf8SyaUf|fc_09ef6deae62cdba9016ad7abfbd77887d2ab1880dc8e184c4d", + "function_name": "bash", + "arguments": { + "command": "sed -i 's/zb\\[mask\\]=dep/zb[mask]=dep[mask]/' /tmp/render_asset.py\npython /tmp/render_asset.py", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_6CRJeDS85SftcwjSJf8SyaUf|fc_09ef6deae62cdba9016ad7abfbd77887d2ab1880dc8e184c4d", + "content": "preview saved", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 30592, + "completion_tokens": 56, + "cached_tokens": 30381, + "extra": { + "cache_write_tokens": 208, + "reasoning_tokens": 0, + "pi_total_tokens": 30648 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "1b4e0e79", + "pi_parent_id": "3d3f6026", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7abf965a087d281b30188948ee5ec", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 24, + "timestamp": "2026-10-20T17:59:31.747Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_h6GaIHRAt3Zhs6dXal4kYMjD|fc_09ef6deae62cdba9016ad7ac03341087d2b944ca8e1f5dd276", + "function_name": "read", + "arguments": { + "path": "/tmp/asset_preview.png", + "offset": 1, + "limit": 1 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_h6GaIHRAt3Zhs6dXal4kYMjD|fc_09ef6deae62cdba9016ad7ac03341087d2b944ca8e1f5dd276", + "content": [ + { + "type": "text", + "text": "Read image file [image/png]" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_2.png" + } + } + ], + "extra": { + "tool_name": "read", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 30662, + "completion_tokens": 30, + "cached_tokens": 30589, + "extra": { + "cache_write_tokens": 70, + "reasoning_tokens": 0, + "pi_total_tokens": 30692 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "eb244016", + "pi_parent_id": "0ab5bc8a", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ac01db1487d28290e34a00baa0d3", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 25, + "timestamp": "2026-10-20T18:00:01.814Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Thinking about design adjustments**\n\nI'm considering giving one hinge axis a slight lean of 2 degrees to allow it to fold over the other, but I'm not sure if that's really necessary. Keeping things simple might be the best approach here. I want to validate this design after applying the patches to ensure everything is functioning correctly. Let's see if that slight adjustment is truly beneficial or if I should stick with a straightforward solution!", + "tool_calls": [ + { + "tool_call_id": "call_HtrbizusYVLcGoQovoTrwRsF|fc_09ef6deae62cdba9016ad7ac1df93887d2b473ab712d30f5b6", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\np='/tmp/build_asset.py';s=open(p).read().replace('range(255)','range(135)').replace('[1,1,2,2,3,4,6,8]','[2,2,3,4,5,7,9,12]')\ns=s.replace(\"if abs(tangent[1])<.8 else np.array([1.,0.,0.])\", \"if abs(tangent[1])<.8 and 'AcetateArm' not in path else np.array([1.,0.,0.])\")\ns=s.replace(\"c.append(knots[-1]);ws.append(.0015);hs.append(.0018)\",\"c.append(knots[-1]);ws.append(.0014);hs.append(.0018)\\n for t in [.35,.65,.85,1.0]:\\n c.append(tuple(np.array(knots[-1])+np.array([0,.0008*t,-.0011*t])));f=math.sqrt(max(.001,1-t*t));ws.append(.0014*f);hs.append(.0018*f)\")\ns=s.replace('-96 if sign<0 else 0','-90 if sign<0 else 0').replace('0 if sign<0 else 96','0 if sign<0 else 90').replace('through 96 degrees','through 90 degrees')\nopen(p,'w').write(s)\np='/tmp/render_asset.py';s=open(p).read().replace('[.29,-.40,.37]','[.40,-.35,.37]').replace('scale=3850','scale=4300');open(p,'w').write(s)\nPY\npython /tmp/build_asset.py && python /tmp/render_asset.py", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_HtrbizusYVLcGoQovoTrwRsF|fc_09ef6deae62cdba9016ad7ac1df93887d2b473ab712d30f5b6", + "content": "Wrote /output/asset.usd\npreview saved", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 31938, + "completion_tokens": 1346, + "cached_tokens": 30659, + "extra": { + "cache_write_tokens": 1276, + "reasoning_tokens": 946, + "pi_total_tokens": 33284 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "e45da867", + "pi_parent_id": "00a0ddaf", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ac04814087d28de2d60351d0934b", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 26, + "timestamp": "2026-10-20T18:00:12.707Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_6l2597zT45wtRfzMM0VftctD|fc_09ef6deae62cdba9016ad7ac2c196887d28d477d105e218f0f", + "function_name": "read", + "arguments": { + "path": "/tmp/asset_preview.png", + "offset": 1, + "limit": 1 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_6l2597zT45wtRfzMM0VftctD|fc_09ef6deae62cdba9016ad7ac2c196887d28d477d105e218f0f", + "content": [ + { + "type": "text", + "text": "Read image file [image/png]" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_3.png" + } + } + ], + "extra": { + "tool_name": "read", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 33307, + "completion_tokens": 30, + "cached_tokens": 31935, + "extra": { + "cache_write_tokens": 1369, + "reasoning_tokens": 0, + "pi_total_tokens": 33337 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "3aa02084", + "pi_parent_id": "68990816", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ac28fc8887d2ab504fa4e8d24fc6", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 27, + "timestamp": "2026-10-20T18:00:49.899Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Analyzing frame dimensions**\n\nI\u2019m considering the ratio for the sunglasses frame, focusing on width and depth dimensions of .174 and .144, respectively. To optimize the mechanical scale, I have to align the front along the Y-axis and the arms along the X-axis. The actual frame dimensions might differ slightly, and achieving an exact image reproduction seems complicated due to perspective. It looks like distance affects how arms project due to camera positioning. So, it\u2019s crucial to factor in these perspective shifts!\n\n**Adjusting camera perspective**\n\nI need to switch from an orthographic to a perspective camera for better accuracy. The left lens is around 160 high and the right around 200. It seems that rendering both orthographically produces the same results. However, when I observe the left lens at a height of 226 and the right at 236, there's a slight wrap projection. Given that the reference heights are 210 and 240, I think a stronger perspective will help maintain the front width dimensions.", + "tool_calls": [ + { + "tool_call_id": "call_SDHmDjuTJPAiKnzhOh9hBGoz|fc_09ef6deae62cdba9016ad7ac516bf487d29ca3ec6e6715da59", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_validate_candidate", + "args": { + "request_id": "check-2" + } + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_SDHmDjuTJPAiKnzhOh9hBGoz|fc_09ef6deae62cdba9016ad7ac516bf487d29ca3ec6e6715da59", + "content": 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I need to handle collider normals in pure rotation. The joint local position seems correct. I\u2019m also considering how body transforms rotate points in world orientation, accounting for offsets. Everything looks good for rotated mesh points and body translations. I have to adjust the front translation as well. The local mass center isn\u2019t the real center, but that's okay. The local rotation for the hinge has no issues either. I've got the metadata description for axes down.", + "tool_calls": [ + { + "tool_call_id": "call_CTvSFm1DUN2MXB1Z7EGABndR|fc_09ef6deae62cdba9016ad7ac99177087d2ae8ea58be7102fd5", + "function_name": "bash", + "arguments": { + "command": "cp /tmp/render_ortho.py /tmp/render_asset.py; sed -i 's/\\[.40,-.35,.37\\]/[-.35,-.40,.37]/' /tmp/render_asset.py\npython /tmp/render_asset.py\npython - <<'PY'\nfrom pxr import Usd,UsdGeom,UsdPhysics,Gf\nimport numpy as np\ns=Usd.Stage.Open('/output/asset.usd');b=UsdGeom.BBoxCache(0,['default']).ComputeWorldBound(s.GetDefaultPrim()).ComputeAlignedBox();print('Bounds:',b,'size',b.GetSize())\nfor p in s.Traverse():\n if p.IsA(UsdPhysics.Joint):\n j=UsdPhysics.Joint(p);a=[]\n for rel,attr in [(j.GetBody0Rel(),j.GetLocalPos0Attr()),(j.GetBody1Rel(),j.GetLocalPos1Attr())]:\n body=s.GetPrimAtPath(rel.GetTargets()[0]);a.append(UsdGeom.XformCache().GetLocalToWorldTransform(body).Transform(Gf.Vec3d(attr.Get())))\n print(p.GetName(),a,'error',(a[0]-a[1]).GetLength())\nPY", + "timeout": 120 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_CTvSFm1DUN2MXB1Z7EGABndR|fc_09ef6deae62cdba9016ad7ac99177087d2ae8ea58be7102fd5", + "content": "preview saved\nBounds: [(-0.07229002242192882, -0.08716034889221191, -4.615465995505552e-11)...(0.07229002708805657, 0.08716034889221191, 0.05692390301305969)] size (0.14458004950998538, 0.17432069778442383, 0.056923903059214354)\nLeftHinge [Gf.Vec3d(-0.06393586521253246, 0.08355554938316345, 0.050099189812728956), Gf.Vec3d(-0.06393586700545692, 0.08355555279164878, 0.05009918846141216)] error 4.0814712336481646e-09\nRightHinge [Gf.Vec3d(-0.06393586521253246, -0.08355554938316345, 0.050099189812728956), Gf.Vec3d(-0.06393586700545692, -0.08355555279164878, 0.05009918846141216)] error 4.0814712336481646e-09", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 42429, + "completion_tokens": 377, + "cached_tokens": 41563, + "extra": { + "cache_write_tokens": 863, + "reasoning_tokens": 87, + "pi_total_tokens": 42806 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "77ff23bc", + "pi_parent_id": "2479fe50", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7ac93e24087d2962cc2c11acf6416", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 34, + "timestamp": "2026-10-20T18:02:11.578Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_5YKV6KXJvq7RhZpJx2gV36zp|fc_09ef6deae62cdba9016ad7aca2f0cc87d2b459238b43babe38", + "function_name": "read", + "arguments": { + "path": "/tmp/asset_preview.png", + "offset": 1, + "limit": 1 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_5YKV6KXJvq7RhZpJx2gV36zp|fc_09ef6deae62cdba9016ad7aca2f0cc87d2b459238b43babe38", + "content": [ + { + "type": "text", + "text": "Read image file [image/png]" + }, + { + "type": "image", + "source": { + "media_type": "image/png", + "path": "images/pi_image_6.png" + } + } + ], + "extra": { + "tool_name": "read", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 43096, + "completion_tokens": 30, + "cached_tokens": 42426, + "extra": { + "cache_write_tokens": 667, + "reasoning_tokens": 0, + "pi_total_tokens": 43126 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "502e3d76", + "pi_parent_id": "be267c89", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aca1dd7487d2a62d274264b46255", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 35, + "timestamp": "2026-10-20T18:02:27.764Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "The repaired asset passed all 46 mandatory NVIDIA checks. The dimensions now match the supplied envelope, and the two hinge attachment frames agree within about 4 nanometres. I\u2019ve also inspected a rendered preview and refined the ear tips and hinge hardware; I\u2019ll run one final validation on those refinements.", + "reasoning_content": "**Planning texture improvements**\n\nI need to consider making some optional updates to mass and inertia. To enhance visual detail, I want to use larger speckles on the acetate, increasing their size while reducing their count. I\u2019m looking at making the speckles on the left arm larger, and the upper brow has too many tiny specks. The focus will be on generating fewer, bigger patches for better accuracy. Overall, I want to validate everything before finalizing, without unnecessary changes afterwards.", + "tool_calls": [ + { + "tool_call_id": "call_kjnh4sbTG1brbReDHQXNpD18|fc_09ef6deae62cdba9016ad7acb30e7487d2be86f600883fddd1", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_validate_candidate", + "args": { + "request_id": "check-final" + } + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_kjnh4sbTG1brbReDHQXNpD18|fc_09ef6deae62cdba9016ad7acb30e7487d2be86f600883fddd1", + "content": "{\"request_id\":\"check-final\",\"snapshot_sha256\":\"4e163912daff86b1c5c62549f69e01dcd151de194e33d57a109f4b06375bf32f\",\"validation_calls_used\":3,\"validation_calls_remaining\":2,\"feedback\":{\"schema_version\":\"simready-validator-feedback-v1\",\"snapshot_sha256\":\"4e163912daff86b1c5c62549f69e01dcd151de194e33d57a109f4b06375bf32f\",\"reward\":100.0,\"simready\":{\"schema\":\"simready-validator-score-v1\",\"policy_id\":\"NVIDIA-Robotics-Prop-4.0.0-mandatory-95-plus-5-v1\",\"status\":\"scored\",\"score_percent\":100.0,\"base_credit_percent\":95.0,\"completion_bonus\":5,\"passed_count\":46,\"failed_count\":0,\"unknown_count\":0,\"applicable_count\":46,\"official_conformance\":{\"profile\":{\"id\":\"Robotics-Prop\",\"version\":\"4.0.0\"},\"passed\":true,\"filtered\":false},\"checks\":[{\"requirement_id\":\"com.nvidia.simready.HI.010\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.005\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.NP.008\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.010\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.RB.COL.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.MAT.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.QCODE.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SL.TIME.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SR.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.SR.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.UN.006\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.UN.007\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.008\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.025\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VG.MESH.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.MAT.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.PS.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.TEX.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.simready.VM.TEX.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.AA.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.AA.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.HI.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.003\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.005\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.009\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.RB.COL.004\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.002\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.014\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.027\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.028\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VG.029\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VM.BIND.001\",\"status\":\"pass\",\"finding_count\":0},{\"requirement_id\":\"com.nvidia.usd.VM.PS.001\",\"status\":\"pass\",\"finding_count\":0}]},\"nvidia_validator_report\":{\"available\":true,\"report_id\":\"2bc2a911e19344e1be2ff9911a1c1cc5b4fde5b576b4982553b6853e9392062c\",\"sha256\":\"2bc2a911e19344e1be2ff9911a1c1cc5b4fde5b576b4982553b6853e9392062c\",\"bytes\":194004,\"total_characters\":194004,\"issue_counts\":{\"issues\":0,\"soft_issues\":49,\"warnings\":0,\"infos\":0},\"complete_report_available\":true,\"delivery\":\"agent_file\",\"file_path\":\"/tmp/simready-validation-reports/2bc2a911e19344e1be2ff9911a1c1cc5b4fde5b576b4982553b6853e9392062c.json\",\"report\":null,\"retrieval_tool\":\"get_validation_report\",\"offset_unit\":\"Unicode_code_points\",\"search_hint\":\"rg -n -m 20 -M 240 --max-columns-preview -F -e -e ; use sed -n ,p for nearby lines\",\"redactions\":\"private host paths and credential patterns only; original diagnostic wording, suggestions and candidate USD sites retained\",\"file_available\":true,\"validation_calls_consumed\":0}},\"status\":\"completed\"}", + "extra": { + "tool_name": "mcp", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 44372, + "completion_tokens": 614, + "cached_tokens": 43093, + "extra": { + "cache_write_tokens": 1276, + "reasoning_tokens": 516, + "pi_total_tokens": 44986 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "ba71200b", + "pi_parent_id": "1f300e51", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7aca4bf3c87d290d0a054cd2aac66", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 36, + "timestamp": "2026-10-20T18:02:35.339Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_5vycmRw2W9Ds4DhQbtZ8Bi7r|fc_09ef6deae62cdba9016ad7acb8d2bc87d2a7be40a649aaf00a", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nimport json,collections\np='/tmp/simready-validation-reports/2bc2a911e19344e1be2ff9911a1c1cc5b4fde5b576b4982553b6853e9392062c.json';r=json.load(open(p));print('Mandatory issues:',r['issues']);print('Warnings:',r['warnings']);print('Optional findings:')\nd=collections.defaultdict(list)\nfor i in r['soft_issues']:d[i['rule_id']].append(i['message'])\nfor k,v in d.items():print(k,len(v),v[0][:350])\nPY\nls -lh /output/asset.usd /output/textures/* /output/.thumbs/256x256/asset.usd.png", + "timeout": 30 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_5vycmRw2W9Ds4DhQbtZ8Bi7r|fc_09ef6deae62cdba9016ad7acb8d2bc87d2a7be40a649aaf00a", + "content": "Mandatory issues: []\nWarnings: []\nOptional findings:\ncom.nvidia.simready.PMT.001 1 Prim has a collision API but no material binding through material:binding:physics.\ncom.nvidia.simready.NVM.002 6 Required attribute \"omni:simready:nonvisual:base\" for nonvisual material doesn't exist.\ncom.nvidia.simready.NVM.003 6 Required attribute \"omni:simready:nonvisual:coating\" for nonvisual material doesn't exist.\ncom.nvidia.simready.NVM.001 6 Required attribute \"omni:simready:nonvisual:attributes\" for nonvisual material doesn't exist.\ncom.nvidia.simready.GSP.001 1 Asset must have at least one grasp vector prim.\ncom.nvidia.simready.VM.PBR.001 6 material '/Sunglasses/Looks/SmokeBlueOpticalGlass' is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'\ncom.nvidia.simready.VM.MDL.003 6 material '/Sunglasses/Looks/SmokeBlueOpticalGlass' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface\ncom.nvidia.simready.ISA.001 2 Default prim must have kind='component' for proper Isaac Sim composition.\ncom.nvidia.simready.RV.007 1 Default prim must declare a 'MuJoCo' physics runtime variant set.\ncom.nvidia.simready.MUJOCO.COL.001 6 MuJoCo collider '/Sunglasses/FrontAssembly/LeftLens' must apply MjcCollisionAPI.\ncom.nvidia.simready.RV.004 1 Default prim must declare a 'Newton' physics runtime variant set.\ncom.nvidia.simready.NEWTON.COL.001 6 Newton mesh collider '/Sunglasses/FrontAssembly/LeftLens' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.\ncom.nvidia.simready.RV.001 1 Default prim must declare a 'PhysX' physics runtime variant set.\n-rw-r--r-- 1 actor actor 16K Oct 20 18:02 /output/.thumbs/256x256/asset.usd.png\n-rw-r--r-- 1 actor actor 400K Oct 20 18:01 /output/asset.usd\n-rw-r--r-- 1 actor actor 604K Oct 20 18:01 /output/textures/acetate.png\n-rw-r--r-- 1 actor actor 596K Oct 20 18:01 /output/textures/temples.png", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 46619, + "completion_tokens": 195, + "cached_tokens": 44369, + "extra": { + "cache_write_tokens": 2247, + "reasoning_tokens": 0, + "pi_total_tokens": 46814 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9b337c7f", + "pi_parent_id": "2bd7bad2", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7acb76ab087d2bad86020fd1036bc", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 37, + "timestamp": "2026-10-20T18:02:38.735Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_qwaA1wknPmfiryWenvVFVzhP|fc_09ef6deae62cdba9016ad7acbe37ec87d29a5dcaff80b7769e", + "function_name": "mcp", + "arguments": { + "tool": "benchmark_submit_asset", + "args": { + "request_id": "final" + } + } + } + ], + "observation": { + "results": [] + }, + "metrics": { + "prompt_tokens": 47412, + "completion_tokens": 36, + "cached_tokens": 46616, + "extra": { + "cache_write_tokens": 793, + "reasoning_tokens": 7, + "pi_total_tokens": 47448 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "7f51fcd0", + "pi_parent_id": "4da79bd4", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_09ef6deae62cdba9016ad7acbca93887d2aa519c0260661d98", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + } + ], + "final_metrics": { + "total_prompt_tokens": 939959, + "total_completion_tokens": 18797, + "total_cached_tokens": 892442, + "total_steps": 37, + "extra": { + "cache_write_tokens": 47409, + "reasoning_tokens": 6802, + "pi_total_tokens": 958756 + } + } +} \ No newline at end of file diff --git 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/opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_001_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_001_STANDARD' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_001_STANDARD-1.0.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_001_STANDARD' version '1.0.1' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_001_STANDARD-1.0.1.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_002_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_002_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_NEWTON' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_NEWTON-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_PHYSX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_PHYSX' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_003_PHYSX-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_003_PHYSX' version '0.3.0' from 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'0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_ROBOT_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_ROBOT_PHYSX' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_ROBOT_PHYSX-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_ROBOT_PHYSX' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_ROBOT_PHYSX-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_ROBOT_PHYSX' version '0.4.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_ROBOT_PHYSX-0.4.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_004_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_004_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_005_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_005_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_MDL' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_MDL-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_MDL' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_MDL-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_OPENPBR' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_OPENPBR-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_006_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_006_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_007_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_007_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_010_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_010_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_011_RTX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_011_RTX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_011_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_011_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_021_ISAAC' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_021_ISAAC-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_ISAAC' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_ISAAC-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_NEWTON' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_NEWTON-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_PHYSX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_PHYSX' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_PHYSX-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_022_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_022_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_023_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_023_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_NEWTON' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_NEWTON-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_PHYSX' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_PHYSX-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_024_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_024_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_025_ROS' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_025_ROS-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_MUJOCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_MUJOCO-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_028_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_028_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_030_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_030_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_031_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_031_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_032_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_032_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_033_STANDARD' version '0.4.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_033_STANDARD-0.4.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_040_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_040_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_041_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_041_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_042_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_042_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_043_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_043_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_044_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_044_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_045_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_045_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_046_STANDARD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_046_STANDARD-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.2.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.3.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_100_ISAAC' version '0.4.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_100_ISAAC-0.4.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering feature 'FET_101_ISAAC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/features/FET_101_ISAAC-0.1.0.json" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-COCO' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-Cityscapes' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-ADE20K' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-PascalVOC' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-SUNRGBD' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Open-Taxonomy-ImageNet1K' version '0.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/open_taxonomy_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-NoBOM' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Package-Candidate' version '1.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/package_profiles.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '2.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Body' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_body.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '2.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robot-Gripper' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robot_gripper.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.1.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.2.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '3.3.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Registering profile 'Robotics-Prop' version '4.0.0' from /opt/simready/venv/lib/python3.12/site-packages/simready/foundation/tier_core/profiles/robotics_prop.toml" + }, + { + "level": "WARNING", + "logger": "root", + "message": "DeprecationWarning: Use find() instead" + }, + { + "level": "WARNING", + "logger": "root", + "message": "File /opt/simready/venv/lib/python3.12/site-packages/simready/validate/api.py, line 485, in _build_features_validation_summary" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NEWTON.COL.001'] did not pass for feature FET_003_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.VM.PBR.001'] did not pass for feature FET_006_OPENPBR." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.MUJOCO.COL.001'] did not pass for feature FET_004_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.MUJOCO.COL.001'] did not pass for feature FET_003_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.RV.004'] did not pass for feature FET_000_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.ISA.001'] did not pass for feature FET_100_ISAAC." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NEWTON.COL.001'] did not pass for feature FET_004_NEWTON." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.NVM.001', 'com.nvidia.simready.NVM.002', 'com.nvidia.simready.NVM.003'] did not pass for feature FET_007_STANDARD." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.RV.001'] did not pass for feature FET_000_PHYSX." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.GSP.001', 'com.nvidia.simready.PMT.001'] did not pass for feature FET_005_STANDARD." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.VM.MDL.003'] did not pass for feature FET_006_MDL." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Optional requirements ['com.nvidia.simready.RV.007'] did not pass for feature FET_000_MUJOCO." + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing requirements registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing features registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing profiles registry" + }, + { + "level": "INFO", + "logger": "simready.validate", + "message": "Clearing rules registry" + } + ], + "outcome": "success", + "passed": true, + "profile_id": "Robotics-Prop", + "profile_version": "4.0.0", + "rule_execution": { + "buckets": [ + { + "bucket": "neutral", + "declared_requirement_codes": [ + "com.nvidia.simready.DISP.001", + "com.nvidia.simready.DISP.002", + "com.nvidia.simready.DISP.003", + "com.nvidia.simready.GSP.001", + "com.nvidia.simready.HI.010", + "com.nvidia.simready.ISA.001", + "com.nvidia.simready.JT.001", + "com.nvidia.simready.JT.ART.003", + "com.nvidia.simready.NP.002", + "com.nvidia.simready.NP.003", + "com.nvidia.simready.NP.004", + "com.nvidia.simready.NP.005", + "com.nvidia.simready.NP.006", + "com.nvidia.simready.NP.007", + "com.nvidia.simready.NP.008", + "com.nvidia.simready.NVM.001", + "com.nvidia.simready.NVM.002", + "com.nvidia.simready.NVM.003", + "com.nvidia.simready.NVM.004", + "com.nvidia.simready.NVM.005", + "com.nvidia.simready.NVM.006", + "com.nvidia.simready.PMT.001", + "com.nvidia.simready.RB.001", + "com.nvidia.simready.RB.007", + "com.nvidia.simready.RB.010", + "com.nvidia.simready.RB.011", + "com.nvidia.simready.RB.012", + "com.nvidia.simready.RB.COL.001", + "com.nvidia.simready.RB.COL.002", + "com.nvidia.simready.RB.COL.003", + "com.nvidia.simready.RB.MB.001", + "com.nvidia.simready.RV.010", + "com.nvidia.simready.RV.011", + "com.nvidia.simready.SL.001", + "com.nvidia.simready.SL.002", + "com.nvidia.simready.SL.003", + "com.nvidia.simready.SL.MAT.001", + "com.nvidia.simready.SL.QCODE.001", + "com.nvidia.simready.SL.TIME.001", + "com.nvidia.simready.SR.001", + "com.nvidia.simready.SR.002", + "com.nvidia.simready.SR.004", + "com.nvidia.simready.UN.006", + "com.nvidia.simready.UN.007", + "com.nvidia.simready.VG.008", + "com.nvidia.simready.VG.025", + "com.nvidia.simready.VG.MESH.001", + "com.nvidia.simready.VM.BIND.002", + "com.nvidia.simready.VM.MAT.001", + "com.nvidia.simready.VM.MDL.001", + "com.nvidia.simready.VM.MDL.003", + "com.nvidia.simready.VM.PBR.001", + "com.nvidia.simready.VM.PBR.002", + "com.nvidia.simready.VM.PBR.003", + "com.nvidia.simready.VM.PS.002", + "com.nvidia.simready.VM.TEX.001", + "com.nvidia.simready.VM.TEX.003", + "com.nvidia.simready.VM.TEX.004", + "com.nvidia.simready.VM.TEX.005", + "com.nvidia.usd.AA.001", + "com.nvidia.usd.AA.002", + "com.nvidia.usd.HI.001", + "com.nvidia.usd.HI.003", + "com.nvidia.usd.HI.004", + "com.nvidia.usd.JT.002", + "com.nvidia.usd.JT.003", + "com.nvidia.usd.JT.ART.002", + "com.nvidia.usd.JT.ART.004", + "com.nvidia.usd.RB.003", + "com.nvidia.usd.RB.005", + "com.nvidia.usd.RB.009", + "com.nvidia.usd.RB.COL.004", + "com.nvidia.usd.VG.002", + "com.nvidia.usd.VG.014", + "com.nvidia.usd.VG.027", + "com.nvidia.usd.VG.028", + "com.nvidia.usd.VG.029", + "com.nvidia.usd.VM.BIND.001", + "com.nvidia.usd.VM.MDL.002", + "com.nvidia.usd.VM.PS.001" + ], + "enabled_rules": [ + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.AssetFolderStructureChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.DirectoryStructureChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.FileNamingConventionChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.MetadataLocationChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.PathLengthLimitsChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.PathsExistChecker", + "simready.foundation.tier_core.capabilities.core.naming_paths.validation.RelativePathsChecker", + "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.RuntimePhysicsIsolationChecker", + "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.RuntimeVariantSectionPurityChecker", + "simready.foundation.tier_core.capabilities.core.sim_ready.validation.MetadataUnion", + 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is bound for rendering but connects no OpenPBR final surface: it connects outputs:surface, and not 'outputs:mtlx:surface'", + "prim_path": "/Sunglasses/Looks/PolishedHingeAlloy", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker", + "rule_id": "com.nvidia.simready.VM.PBR.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/PolishedHingeAlloy)", + "message": "material '/Sunglasses/Looks/PolishedHingeAlloy' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface", + "prim_path": "/Sunglasses/Looks/PolishedHingeAlloy", + "requirement_version": "0.1.0", + "rule": 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"StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/Looks/AcetateWithMakerMark)", + "message": "material '/Sunglasses/Looks/AcetateWithMakerMark' is bound for rendering and connects no 'outputs:mdl:surface', so the asset does not have an MDL final surface on the geometry it renders: it connects outputs:surface", + "prim_path": "/Sunglasses/Looks/AcetateWithMakerMark", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.visualization.materials.validation.VisualMaterialsCapabilityChecker", + "rule_id": "com.nvidia.simready.VM.MDL.003", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses)", + "message": "Default prim must have kind='component' for proper Isaac Sim composition.", + "prim_path": "/Sunglasses", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.isaac_sim.composition.validation.IsaacCompositionCapabilityChecker", + "rule_id": "com.nvidia.simready.ISA.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses)", + "message": "Incomplete Isaac Sim composition: performance robot layout; missing layers: base, geometries, instances, materials.", + "prim_path": "/Sunglasses", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.isaac_sim.composition.validation.IsaacCompositionCapabilityChecker", + "rule_id": "com.nvidia.simready.ISA.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses)", + "message": "Default prim must declare a 'MuJoCo' physics runtime variant set.", + "prim_path": "/Sunglasses", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.MuJoCoVariantSetChecker", + "rule_id": "com.nvidia.simready.RV.007", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/FrontAssembly/LeftLens)", + "message": "MuJoCo collider '/Sunglasses/FrontAssembly/LeftLens' must apply MjcCollisionAPI.", + "prim_path": "/Sunglasses/FrontAssembly/LeftLens", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.MuJoCoColliderAPIChecker", + "rule_id": 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"failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/LeftTemple/AcetateArm)", + "message": "MuJoCo collider '/Sunglasses/LeftTemple/AcetateArm' must apply MjcCollisionAPI.", + "prim_path": "/Sunglasses/LeftTemple/AcetateArm", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.MuJoCoColliderAPIChecker", + "rule_id": "com.nvidia.simready.MUJOCO.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/RightTemple/AcetateArm)", + "message": "MuJoCo collider '/Sunglasses/RightTemple/AcetateArm' must apply MjcCollisionAPI.", + "prim_path": "/Sunglasses/RightTemple/AcetateArm", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.MuJoCoColliderAPIChecker", + "rule_id": "com.nvidia.simready.MUJOCO.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses)", + "message": "Default prim must declare a 'Newton' physics runtime variant set.", + "prim_path": "/Sunglasses", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.core.runtime_variants.validation.NewtonVariantSetChecker", + "rule_id": "com.nvidia.simready.RV.004", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/FrontAssembly/LeftLens)", + "message": "Newton mesh collider '/Sunglasses/FrontAssembly/LeftLens' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.", + "prim_path": "/Sunglasses/FrontAssembly/LeftLens", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.NewtonColliderAPIChecker", + "rule_id": "com.nvidia.simready.NEWTON.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/FrontAssembly/LeftOuterCorner)", + "message": "Newton mesh collider '/Sunglasses/FrontAssembly/LeftOuterCorner' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.", + "prim_path": 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"StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/FrontAssembly/RightOuterCorner)", + "message": "Newton mesh collider '/Sunglasses/FrontAssembly/RightOuterCorner' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.", + "prim_path": "/Sunglasses/FrontAssembly/RightOuterCorner", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.NewtonColliderAPIChecker", + "rule_id": "com.nvidia.simready.NEWTON.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/LeftTemple/AcetateArm)", + "message": "Newton mesh collider '/Sunglasses/LeftTemple/AcetateArm' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.", + "prim_path": "/Sunglasses/LeftTemple/AcetateArm", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.NewtonColliderAPIChecker", + "rule_id": "com.nvidia.simready.NEWTON.COL.001", + "severity": "failure", + "suggestions": [] + }, + { + "asset": "StageId(root_layer=LayerId(identifier='/output/asset.usd'))", + "location": "PrimId(stage_id=StageId(root_layer=LayerId(identifier='/output/asset.usd')), path=/Sunglasses/RightTemple/AcetateArm)", + "message": "Newton mesh collider '/Sunglasses/RightTemple/AcetateArm' must apply either NewtonMeshCollisionAPI or NewtonSDFCollisionAPI.", + "prim_path": "/Sunglasses/RightTemple/AcetateArm", + "requirement_version": "0.1.0", + "rule": "simready.foundation.tier_core.capabilities.physics_bodies.physics_rigid_bodies.validation.NewtonColliderAPIChecker", + "rule_id": "com.nvidia.simready.NEWTON.COL.001", + "severity": 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a/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/report.html +++ b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/report.html @@ -1 +1 @@ -SimReadyBench results

SimReadyBench results

Formal fidelity measures SimReady Robotics-Prop requirements. Visual fidelity combines shape and appearance materials. Kinematic fidelity measures joint recovery and motion correspondence. Each score is reported independently.

7 of 100 reconstruction attempts completed.

Scores CSV | Detailed results JSON

Results by asset

AssetModelHarnessSkillsRepetitionStatusFormal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
accessory_eyeglasses_a01gpt-6.1-solpinone1completed100% (46/46)13.76% (0.27519/2)0% (0/2)66.67% (34/51)49.39% (0.493932/1)18.61% (0.744535/4)0% (0/29)7.28% (1117/15341)
box_cardboard_open_a01gpt-6.1-solpinone1completed100% (46/46)0% (0/2)N/A74.51% (38/51)79.78% (0.797763/1)79.78% (0.797763/1)N/A31.43% (4506/14336)
box_cardboard_open_a01gpt-6.1-solpinone2completed100% (46/46)81.15% (1.62306/2)N/A74.51% (38/51)83.1% (0.831036/1)83.1% (0.831036/1)N/A45.28% (6492/14336)
bucket_utility_red_a01gpt-6.1-solpinone1completed100% (46/46)0% (0/2)0.11% (0.00114342/1)76.47% (39/51)68.23% (0.682344/1)43.94% (0.878841/2)72.73% (8/11)31.89% (5216/16356)
bucket_utility_red_a01gpt-6.1-solpinone2completed100% (46/46)0% (0/2)0.03% (0.000286204/1)70.59% (36/51)0% (0/1)0% (0/2)72.73% (8/11)6.35% (1038/16356)
chair_office_amf_black_a01gpt-6.1-solpinone1completed100% (46/46)46.68% (0.933616/2)0% (0/1)72.55% (37/51)69.57% (0.695681/1)17.92% (0.17916/1)N/A17.65% (2891/16384)
chair_office_amf_black_a01gpt-6.1-solpinone2completed100% (46/46)73.34% (1.46678/2)0% (0/1)74.51% (38/51)67.58% (0.67579/1)15.34% (0.153366/1)N/A29.85% (4891/16384)

accessory_eyeglasses_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)13.76% (0.27519/2)0% (0/2)66.67% (34/51)49.39% (0.493932/1)18.61% (0.744535/4)0% (0/29)7.28% (1117/15341)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape49.390.493932/111100
Rigid parts18.610.744535/444100
Joints00/292929100
Materials7.281117/15341153411534162.42

Appearance materials

Score (%)Earned/eligibleStatus
3.8378.5/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/11/1100
Moving joints00/44/4100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_arm_02_joint_00Not measured0Not measuredmissing_joint
/RootNode/Joints/joint_arm_01_joint_00Not measured0Not measuredmissing_joint
/RootNode/Joints/joint_glass_01_joint_00Not measured0Not measuredmissing_joint

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
52.246.8820483.349.6223.22

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/arm_01_obj_00/Sunglasses/RightTemple0
/RootNode/Geometry/arm_02_obj_00/Sunglasses/LeftTemple0
/RootNode/Geometry/base_01_obj_00Not measured0
/RootNode/Geometry/glass_01_obj_00/Sunglasses/Front74.45

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
320/29

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled3/44/4
body_kinematic3/44/4

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity0/33/3
joint_enabled0/33/3
body0_enabled0/33/3
body0_kinematic0/33/3
body1_enabled0/33/3
body1_kinematic0/33/3
anchors0/33/3
axes0/22/2
relative_orientation0/33/3
limits0/22/2
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment960/204846.88
visual:metallic157/100515.62
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

accessory_eyeglasses_a01: Reference: front
Reference: front | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
accessory_eyeglasses_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
accessory_eyeglasses_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
accessory_eyeglasses_a01: Reference: side
Reference: side | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

box_cardboard_open_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)N/A74.51% (38/51)79.78% (0.797763/1)79.78% (0.797763/1)N/A31.43% (4506/14336)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape79.780.797763/111100
Rigid parts79.780.797763/111100
JointsNot applicable0/0000
Materials31.434506/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
87.4573.3420489.76.7613.69

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_box_a01_obj_00/CardboardTray79.78

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1502/204873.34
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction1502/204873.34
visual:assignment1502/204873.34
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

box_cardboard_open_a01: Reference: front
Reference: front | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
box_cardboard_open_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
box_cardboard_open_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
box_cardboard_open_a01: Reference: side
Reference: side | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

box_cardboard_open_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)81.15% (1.62306/2)N/A74.51% (38/51)83.1% (0.831036/1)83.1% (0.831036/1)N/A45.28% (6492/14336)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape83.10.831036/111100
Rigid parts83.10.831036/111100
JointsNot applicable0/0000
Materials45.286492/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
79.251623/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
87.3579.2520489.76.3113.02

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_box_a01_obj_00/Asset/Tray83.1

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1623/204879.25
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction1623/204879.25
visual:assignment1623/204879.25
visual:opacity1623/204879.25

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

box_cardboard_open_a01: Reference: front
Reference: front | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
box_cardboard_open_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
box_cardboard_open_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
box_cardboard_open_a01: Reference: side
Reference: side | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2

bucket_utility_red_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)0.11% (0.00114342/1)76.47% (39/51)68.23% (0.682344/1)43.94% (0.878841/2)72.73% (8/11)31.89% (5216/16356)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape68.230.682344/111100
Rigid parts43.940.878841/222100
Joints72.738/111111100
Materials31.895216/16356163561635666.55

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving joints0.110.00343026/33/3100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_sm_handle_a01_01_joint_00/Bucket/Joints/BailPivot0.1769.38measured_motion

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
56.8485.3520486.386.3212.09

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_bucket_utility_a01_obj_00/Bucket/Pail67.9
/RootNode/Geometry/sm_handle_a01_01_obj_00/Bucket/Bail19.99

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
128/11

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled2/22/2
body_kinematic2/22/2

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity1/11/1
joint_enabled1/11/1
body0_enabled1/11/1
body0_kinematic1/11/1
body1_enabled1/11/1
body1_kinematic1/11/1
anchors0/11/1
axes1/11/1
relative_orientation1/11/1
limits0/11/1
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1748/204885.35
physics:density0/20480
physics:dynamicFriction1714/204883.69
physics:restitution0/20480
physics:staticFriction6/20480.29
visual:assignment1748/204885.35
visual:diffuseColor0/20200
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

bucket_utility_red_a01: Reference: front
Reference: front | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
bucket_utility_red_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
bucket_utility_red_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
bucket_utility_red_a01: Reference: side
Reference: side | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

bucket_utility_red_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)0.03% (0.000286204/1)70.59% (36/51)0% (0/1)0% (0/2)72.73% (8/11)6.35% (1038/16356)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/222100
Joints72.738/111111100
Materials6.351038/16356163561635666.55

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving joints0.030.000858611/33/3100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_sm_handle_a01_01_joint_00/Bucket/Joints/BailPivot0.04234.01measured_motion

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
35.3550.6820486.389.8916.05

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_bucket_utility_a01_obj_00/Bucket/Body0
/RootNode/Geometry/sm_handle_a01_01_obj_00/Bucket/Bail0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
128/11

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled2/22/2
body_kinematic2/22/2

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity1/11/1
joint_enabled1/11/1
body0_enabled1/11/1
body0_kinematic1/11/1
body1_enabled1/11/1
body1_kinematic1/11/1
anchors1/11/1
axes0/11/1
relative_orientation1/11/1
limits0/11/1
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1038/204850.68
visual:diffuseColor0/20200
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

bucket_utility_red_a01: Reference: front
Reference: front | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
bucket_utility_red_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
bucket_utility_red_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
bucket_utility_red_a01: Reference: side
Reference: side | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2

chair_office_amf_black_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)46.68% (0.933616/2)0% (0/1)72.55% (37/51)69.57% (0.695681/1)17.92% (0.17916/1)N/A17.65% (2891/16384)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape69.570.695681/111100
Rigid parts17.920.17916/111100
JointsNot applicable0/0000
Materials17.652891/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
31.32641.5/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/22/2100
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
62.4578.52204812.2915.5134.93

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_chair_office_amf_a01_obj_00/Chair/SteelFrame17.92

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
020/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1608/204878.52
visual:diffuseColor1283/204862.65
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

chair_office_amf_black_a01: Reference: front
Reference: front | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
chair_office_amf_black_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
chair_office_amf_black_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
chair_office_amf_black_a01: Reference: side
Reference: side | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

chair_office_amf_black_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)73.34% (1.46678/2)0% (0/1)74.51% (38/51)67.58% (0.67579/1)15.34% (0.153366/1)N/A29.85% (4891/16384)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape67.580.67579/111100
Rigid parts15.340.153366/111100
JointsNot applicable0/0000
Materials29.854891/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
79.591630/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/33/3100
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
58.6979.64204812.2920.8444.98

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_chair_office_amf_a01_obj_00/Chair/SteelFrame15.34

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
030/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1631/204879.64
visual:diffuseColor1629/204879.54
visual:opacity1631/204879.64

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

chair_office_amf_black_a01: Reference: front
Reference: front | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
chair_office_amf_black_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
chair_office_amf_black_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
chair_office_amf_black_a01: Reference: side
Reference: side | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2
\ No newline at end of file +SimReadyBench results

SimReadyBench results

Formal fidelity measures SimReady Robotics-Prop requirements. Visual fidelity combines shape and appearance materials. Kinematic fidelity measures joint recovery and motion correspondence. Each score is reported independently.

8 of 100 reconstruction attempts completed.

Scores CSV | Detailed results JSON

Results by asset

AssetModelHarnessSkillsRepetitionStatusFormal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
accessory_eyeglasses_a01gpt-6.1-solpinone1completed100% (46/46)13.76% (0.27519/2)0% (0/2)66.67% (34/51)49.39% (0.493932/1)18.61% (0.744535/4)0% (0/29)7.28% (1117/15341)
accessory_eyeglasses_a01gpt-6.1-solpinone2completed100% (46/46)0% (0/2)0% (0/2)74.51% (38/51)74.97% (0.749701/1)37.71% (1.50839/4)62.07% (18/29)9.44% (1448/15341)
box_cardboard_open_a01gpt-6.1-solpinone1completed100% (46/46)0% (0/2)N/A74.51% (38/51)79.78% (0.797763/1)79.78% (0.797763/1)N/A31.43% (4506/14336)
box_cardboard_open_a01gpt-6.1-solpinone2completed100% (46/46)81.15% (1.62306/2)N/A74.51% (38/51)83.1% (0.831036/1)83.1% (0.831036/1)N/A45.28% (6492/14336)
bucket_utility_red_a01gpt-6.1-solpinone1completed100% (46/46)0% (0/2)0.11% (0.00114342/1)76.47% (39/51)68.23% (0.682344/1)43.94% (0.878841/2)72.73% (8/11)31.89% (5216/16356)
bucket_utility_red_a01gpt-6.1-solpinone2completed100% (46/46)0% (0/2)0.03% (0.000286204/1)70.59% (36/51)0% (0/1)0% (0/2)72.73% (8/11)6.35% (1038/16356)
chair_office_amf_black_a01gpt-6.1-solpinone1completed100% (46/46)46.68% (0.933616/2)0% (0/1)72.55% (37/51)69.57% (0.695681/1)17.92% (0.17916/1)N/A17.65% (2891/16384)
chair_office_amf_black_a01gpt-6.1-solpinone2completed100% (46/46)73.34% (1.46678/2)0% (0/1)74.51% (38/51)67.58% (0.67579/1)15.34% (0.153366/1)N/A29.85% (4891/16384)

accessory_eyeglasses_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)13.76% (0.27519/2)0% (0/2)66.67% (34/51)49.39% (0.493932/1)18.61% (0.744535/4)0% (0/29)7.28% (1117/15341)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape49.390.493932/111100
Rigid parts18.610.744535/444100
Joints00/292929100
Materials7.281117/15341153411534162.42

Appearance materials

Score (%)Earned/eligibleStatus
3.8378.5/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/11/1100
Moving joints00/44/4100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_arm_02_joint_00Not measured0Not measuredmissing_joint
/RootNode/Joints/joint_arm_01_joint_00Not measured0Not measuredmissing_joint
/RootNode/Joints/joint_glass_01_joint_00Not measured0Not measuredmissing_joint

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
52.246.8820483.349.6223.22

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/arm_01_obj_00/Sunglasses/RightTemple0
/RootNode/Geometry/arm_02_obj_00/Sunglasses/LeftTemple0
/RootNode/Geometry/base_01_obj_00Not measured0
/RootNode/Geometry/glass_01_obj_00/Sunglasses/Front74.45

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
320/29

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled3/44/4
body_kinematic3/44/4

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity0/33/3
joint_enabled0/33/3
body0_enabled0/33/3
body0_kinematic0/33/3
body1_enabled0/33/3
body1_kinematic0/33/3
anchors0/33/3
axes0/22/2
relative_orientation0/33/3
limits0/22/2
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment960/204846.88
visual:metallic157/100515.62
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

accessory_eyeglasses_a01: Reference: front
Reference: front | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
accessory_eyeglasses_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
accessory_eyeglasses_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
accessory_eyeglasses_a01: Reference: side
Reference: side | pi | none | repetition 1
accessory_eyeglasses_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

accessory_eyeglasses_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)0% (0/2)74.51% (38/51)74.97% (0.749701/1)37.71% (1.50839/4)62.07% (18/29)9.44% (1448/15341)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape74.970.749701/111100
Rigid parts37.711.50839/444100
Joints62.0718/292929100
Materials9.441448/15341153411534162.42

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/11/1100
Moving joints35.91.43593/44/4100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_arm_02_joint_00/Sunglasses/LeftHinge38.644.2measured_motion
/RootNode/Joints/joint_arm_01_joint_00/Sunglasses/RightHinge33.164.74measured_motion
/RootNode/Joints/joint_glass_01_joint_00Not measured0Not measuredmissing_joint

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
79.7970.720483.343.898.25

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/arm_01_obj_00/Sunglasses/RightTemple55.99
/RootNode/Geometry/arm_02_obj_00/Sunglasses/LeftTemple58.23
/RootNode/Geometry/base_01_obj_00/Sunglasses/FrontAssembly36.63
/RootNode/Geometry/glass_01_obj_00Not measured0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
3218/29

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled3/44/4
body_kinematic3/44/4

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity2/33/3
joint_enabled2/33/3
body0_enabled2/33/3
body0_kinematic2/33/3
body1_enabled2/33/3
body1_kinematic2/33/3
anchors0/33/3
axes2/22/2
relative_orientation2/33/3
limits2/22/2
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1448/204870.7
visual:metallic0/10050
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

accessory_eyeglasses_a01: Reference: front
Reference: front | pi | none | repetition 2
accessory_eyeglasses_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
accessory_eyeglasses_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
accessory_eyeglasses_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
accessory_eyeglasses_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
accessory_eyeglasses_a01: Reference: side
Reference: side | pi | none | repetition 2
accessory_eyeglasses_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2

box_cardboard_open_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)N/A74.51% (38/51)79.78% (0.797763/1)79.78% (0.797763/1)N/A31.43% (4506/14336)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape79.780.797763/111100
Rigid parts79.780.797763/111100
JointsNot applicable0/0000
Materials31.434506/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
87.4573.3420489.76.7613.69

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_box_a01_obj_00/CardboardTray79.78

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1502/204873.34
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction1502/204873.34
visual:assignment1502/204873.34
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

box_cardboard_open_a01: Reference: front
Reference: front | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
box_cardboard_open_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
box_cardboard_open_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
box_cardboard_open_a01: Reference: side
Reference: side | pi | none | repetition 1
box_cardboard_open_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

box_cardboard_open_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)81.15% (1.62306/2)N/A74.51% (38/51)83.1% (0.831036/1)83.1% (0.831036/1)N/A45.28% (6492/14336)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape83.10.831036/111100
Rigid parts83.10.831036/111100
JointsNot applicable0/0000
Materials45.286492/14336143361433658.33

Appearance materials

Score (%)Earned/eligibleStatus
79.251623/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
87.3579.2520489.76.3113.02

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_box_a01_obj_00/Asset/Tray83.1

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
000/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count1/11/1
joint_inventory_count1/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1623/204879.25
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction1623/204879.25
visual:assignment1623/204879.25
visual:opacity1623/204879.25

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

box_cardboard_open_a01: Reference: front
Reference: front | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
box_cardboard_open_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
box_cardboard_open_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
box_cardboard_open_a01: Reference: side
Reference: side | pi | none | repetition 2
box_cardboard_open_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2

bucket_utility_red_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)0.11% (0.00114342/1)76.47% (39/51)68.23% (0.682344/1)43.94% (0.878841/2)72.73% (8/11)31.89% (5216/16356)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape68.230.682344/111100
Rigid parts43.940.878841/222100
Joints72.738/111111100
Materials31.895216/16356163561635666.55

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving joints0.110.00343026/33/3100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_sm_handle_a01_01_joint_00/Bucket/Joints/BailPivot0.1769.38measured_motion

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
56.8485.3520486.386.3212.09

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_bucket_utility_a01_obj_00/Bucket/Pail67.9
/RootNode/Geometry/sm_handle_a01_01_obj_00/Bucket/Bail19.99

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
128/11

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled2/22/2
body_kinematic2/22/2

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity1/11/1
joint_enabled1/11/1
body0_enabled1/11/1
body0_kinematic1/11/1
body1_enabled1/11/1
body1_kinematic1/11/1
anchors0/11/1
axes1/11/1
relative_orientation1/11/1
limits0/11/1
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment1748/204885.35
physics:density0/20480
physics:dynamicFriction1714/204883.69
physics:restitution0/20480
physics:staticFriction6/20480.29
visual:assignment1748/204885.35
visual:diffuseColor0/20200
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

bucket_utility_red_a01: Reference: front
Reference: front | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
bucket_utility_red_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
bucket_utility_red_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
bucket_utility_red_a01: Reference: side
Reference: side | pi | none | repetition 1
bucket_utility_red_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

bucket_utility_red_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)0% (0/2)0.03% (0.000286204/1)70.59% (36/51)0% (0/1)0% (0/2)72.73% (8/11)6.35% (1038/16356)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape00/111100
Rigid parts00/222100
Joints72.738/111111100
Materials6.351038/16356163561635666.55

Appearance materials

Score (%)Earned/eligibleStatus
00/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraintsNot measured0/00/00
Moving joints0.030.000858611/33/3100
Reference jointCandidate jointScore (%)Motion RMS (mm)Status
/RootNode/Joints/joint_sm_handle_a01_01_joint_00/Bucket/Joints/BailPivot0.04234.01measured_motion

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
35.3550.6820486.389.8916.05

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_bucket_utility_a01_obj_00/Bucket/Body0
/RootNode/Geometry/sm_handle_a01_01_obj_00/Bucket/Bail0

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
128/11

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled2/22/2
body_kinematic2/22/2

Joint checks

FeaturePassed/eligibleMeasured/eligible
existence_type_connectivity1/11/1
joint_enabled1/11/1
body0_enabled1/11/1
body0_kinematic1/11/1
body1_enabled1/11/1
body1_kinematic1/11/1
anchors1/11/1
axes0/11/1
relative_orientation1/11/1
limits0/11/1
joint_count0/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1038/204850.68
visual:diffuseColor0/20200
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

bucket_utility_red_a01: Reference: front
Reference: front | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
bucket_utility_red_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
bucket_utility_red_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
bucket_utility_red_a01: Reference: side
Reference: side | pi | none | repetition 2
bucket_utility_red_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2

chair_office_amf_black_a01 / gpt-6.1-sol / repetition 1

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone1completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)46.68% (0.933616/2)0% (0/1)72.55% (37/51)69.57% (0.695681/1)17.92% (0.17916/1)N/A17.65% (2891/16384)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape69.570.695681/111100
Rigid parts17.920.17916/111100
JointsNot applicable0/0000
Materials17.652891/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
31.32641.5/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/22/2100
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
62.4578.52204812.2915.5134.93

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_chair_office_amf_a01_obj_00/Chair/SteelFrame17.92

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
020/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1608/204878.52
visual:diffuseColor1283/204862.65
visual:opacity0/20480

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

chair_office_amf_black_a01: Reference: front
Reference: front | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 1
chair_office_amf_black_a01: Reference: isometric
Reference: isometric | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 1
chair_office_amf_black_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 1
chair_office_amf_black_a01: Reference: side
Reference: side | pi | none | repetition 1
chair_office_amf_black_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 1

chair_office_amf_black_a01 / gpt-6.1-sol / repetition 2

ModelHarnessSkillsRepetitionStatus
gpt-6.1-solpinone2completed
Formal fidelity (%)Visual fidelity (%)Kinematic fidelity (%)SimReady Robotics-Prop optionalShape (%)Rigid parts (%)Joints (%)Materials (%)
100% (46/46)73.34% (1.46678/2)0% (0/1)74.51% (38/51)67.58% (0.67579/1)15.34% (0.153366/1)N/A29.85% (4891/16384)

46 of 46 mandatory requirements passed.

Direct USD reconstruction fidelity

ComponentFidelity (%)CreditMeasuredEligibleReference coverage (%)
Shape67.580.67579/111100
Rigid parts15.340.153366/111100
JointsNot applicable0/0000
Materials29.854891/16384163841638466.67

Appearance materials

Score (%)Earned/eligibleStatus
79.591630/2048measured

Kinematic correspondence

FactorScore (%)Earned/eligibleMeasured/eligibleCoverage (%)
Fixed constraints00/33/3100
Moving jointsNot measured0/00/00
Reference jointCandidate jointScore (%)Motion RMS (mm)Status

Surface correspondence

Precision (%)Recall (%)Samples per directionDistance band (mm)RMS distance (mm)95th-percentile distance (mm)
58.6979.64204812.2920.8444.98

Rigid-part correspondence

Reference bodyCandidate bodySurface correspondence (%)
/RootNode/Geometry/sm_chair_office_amf_a01_obj_00/Chair/SteelFrame15.34

Joint correspondence

Reference jointsCandidate jointsPassed/eligible checks
030/0

Rigid-structure checks

FeaturePassed/eligibleMeasured/eligible
body_count0/11/1
joint_inventory_count0/11/1
body_enabled1/11/1
body_kinematic1/11/1

Material correspondence

PropertyCredit/eligibleCorrespondence (%)
physics:assignment0/20480
physics:density0/20480
physics:dynamicFriction0/20480
physics:restitution0/20480
physics:staticFriction0/20480
visual:assignment1631/204879.64
visual:diffuseColor1629/204879.54
visual:opacity1631/204879.64

Generated USD | Asset package manifest | NVIDIA validation findings | Harbor trajectory 1

Matched captures

chair_office_amf_black_a01: Reference: front
Reference: front | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: front
Reconstruction: front | pi | none | repetition 2
chair_office_amf_black_a01: Reference: isometric
Reference: isometric | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: isometric
Reconstruction: isometric | pi | none | repetition 2
chair_office_amf_black_a01: matched reference: reconstruction contact sheet
matched reference: reconstruction contact sheet | pi | none | repetition 2
chair_office_amf_black_a01: Reference: side
Reference: side | pi | none | repetition 2
chair_office_amf_black_a01: Reconstruction: side
Reconstruction: side | pi | none | repetition 2
\ No newline at end of file diff --git a/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/results.json b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/results.json index f044ff104ae4703763f60e9eaa737405ac2fc8e7..8bea901cbffd9fd70f55afeeb7372e59b0c437fc 100644 --- a/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/results.json +++ b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/results.json @@ -1,7 +1,7 @@ { "schema": "simready-bench-portable-report-v1", "planned_conditions": 100, - "completed_conditions": 7, + "completed_conditions": 8, "partial": true, "settings": { "reference_assets": [ @@ -2509,6 +2509,2509 @@ "bytes": 246846 } }, + { + "condition_id": "condition-e6cdba5720a53864bb1be8577a689196092e0262f91241ec77ad601c5284cbb1", + "asset_id": "accessory_eyeglasses_a01", + "requested_model": "gpt-6.1-sol", + "harness_id": "pi", + "skill_set_id": "none", + "repetition": 1, + "status": "completed", + "formal_fidelity_percent": 100.0, + "visual_fidelity_percent": 0.0, + "kinematic_fidelity_percent": 0.0, + "simready_reward": 100.0, + "mandatory_passed": 46, + "mandatory_total": 46, + "mandatory_failed": 0, + "mandatory_unknown": 0, + "mandatory_lower_bound_percent": 100.0, + "mandatory_upper_bound_percent": 100.0, + "completion_bonus": 5, + "optional_score_percent": 74.50980392156863, + "optional_passed": 38, + "optional_failed": 13, + "optional_unknown": 0, + "optional_total": 51, + "optional_measured": 51, + "optional_coverage": 1.0, + "optional_lower_bound_percent": 74.50980392156863, + "optional_upper_bound_percent": 74.50980392156863, + "fidelity_shape_percent": 74.97008841661258, + "fidelity_rigid_parts_percent": 37.70981401625411, + "fidelity_joints_percent": 62.06896551724138, + "fidelity_materials_percent": 9.438758881428852, + "fidelity_shape_coverage": 1.0, + "fidelity_rigid_parts_coverage": 1.0, + "fidelity_joints_coverage": 1.0, + "fidelity_materials_coverage": 0.6242268880208334, + "fidelity_reference_geometry_status": "complete", + "formal_fidelity_status": "measured", + "formal_fidelity_earned": 46, + "formal_fidelity_eligible": 46, + "formal_fidelity_measured": 46, + "formal_fidelity_coverage": 1.0, + "formal_fidelity_lower_bound_percent": 100.0, + "formal_fidelity_upper_bound_percent": 100.0, + "visual_fidelity_status": "measured", + "visual_fidelity_earned": 0.0, + "visual_fidelity_eligible": 2, + "visual_fidelity_measured": 2, + "visual_fidelity_coverage": 1.0, + "visual_fidelity_lower_bound_percent": 0.0, + "visual_fidelity_upper_bound_percent": 0.0, + "kinematic_fidelity_status": "measured", + "kinematic_fidelity_earned": 0.0, + "kinematic_fidelity_eligible": 2, + "kinematic_fidelity_measured": 2, + "kinematic_fidelity_coverage": 1.0, + "kinematic_fidelity_lower_bound_percent": 0.0, + "kinematic_fidelity_upper_bound_percent": 0.0, + "visual_appearance_percent": 0.0, + "visual_appearance_status": "measured", + "visual_appearance_earned": 0.0, + 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a/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/scores.csv b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/scores.csv index f7d5a20165a7e90c9429faa22d04e4de636230f2..60212cc9bba23abbd1a1302a9477ebc74ea0a4c1 100644 --- a/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/scores.csv +++ b/runs/fresh-v1-nv25-gpt-6.1-sol-pi-none-medium-turn64-20261020/scores.csv @@ -1,5 +1,6 @@ condition_id,asset_id,requested_model,harness_id,skill_set_id,repetition,status,formal_fidelity_percent,visual_fidelity_percent,kinematic_fidelity_percent,simready_reward,mandatory_passed,mandatory_total,mandatory_failed,mandatory_unknown,mandatory_lower_bound_percent,mandatory_upper_bound_percent,completion_bonus,optional_score_percent,optional_passed,optional_failed,optional_unknown,optional_total,optional_measured,optional_coverage,optional_lower_bound_percent,optional_upper_bound_percent,fidelity_shape_percent,fidelity_rigid_parts_percent,fidelity_joints_percent,fidelity_materials_percent,fidelity_shape_coverage,fidelity_rigid_parts_coverage,fidelity_joints_coverage,fidelity_materials_coverage,fidelity_reference_geometry_status,formal_fidelity_status,formal_fidelity_earned,formal_fidelity_eligible,formal_fidelity_measured,formal_fidelity_coverage,formal_fidelity_lower_bound_percent,formal_fidelity_upper_bound_percent,visual_fidelity_status,visual_fidelity_earned,visual_fidelity_eligible,visual_fidelity_measured,visual_fidelity_coverage,visual_fidelity_lower_bound_percent,visual_fidelity_upper_bound_percent,kinematic_fidelity_status,kinematic_fidelity_earned,kinematic_fidelity_eligible,kinematic_fidelity_measured,kinematic_fidelity_coverage,kinematic_fidelity_lower_bound_percent,kinematic_fidelity_upper_bound_percent,visual_appearance_percent,visual_appearance_status,visual_appearance_earned,visual_appearance_eligible,visual_appearance_measured,visual_appearance_coverage,visual_appearance_lower_bound_percent,visual_appearance_upper_bound_percent,kinematic_reference_joint_count,kinematic_candidate_joint_count,kinematic_matched_joint_count,kinematic_missing_joint_count,kinematic_extra_joint_count,kinematic_moving_joints_percent,kinematic_moving_joints_status,kinematic_moving_joints_earned,kinematic_moving_joints_eligible,kinematic_moving_joints_measured,kinematic_moving_joints_coverage,kinematic_moving_joints_lower_bound_percent,kinematic_moving_joints_upper_bound_percent,kinematic_fixed_constraints_percent,kinematic_fixed_constraints_status,kinematic_fixed_constraints_earned,kinematic_fixed_constraints_eligible,kinematic_fixed_constraints_measured,kinematic_fixed_constraints_coverage,kinematic_fixed_constraints_lower_bound_percent,kinematic_fixed_constraints_upper_bound_percent,fidelity_shape_earned,fidelity_shape_eligible,fidelity_shape_measured,fidelity_shape_status,fidelity_rigid_parts_earned,fidelity_rigid_parts_eligible,fidelity_rigid_parts_measured,fidelity_rigid_parts_status,fidelity_joints_earned,fidelity_joints_eligible,fidelity_joints_measured,fidelity_joints_status,fidelity_materials_earned,fidelity_materials_eligible,fidelity_materials_measured,fidelity_materials_status,fidelity_shape_precision,fidelity_shape_recall,fidelity_shape_samples_each_direction,fidelity_rigid_parts_geometry_percent,fidelity_rigid_parts_structure_percent,fidelity_rigid_parts_structure_earned,fidelity_rigid_parts_structure_eligible,fidelity_joints_reference_count,fidelity_joints_candidate_count 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