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/output/asset.usd.\nNominal dimensions in canonical metres XYZ: [0.2163844726378463, 0.08936920017004013, 0.24394267840034445]\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": "c75320aa", + "pi_parent_id": "6786ef18" + } + }, + { + "step_id": 2, + "timestamp": "2026-10-21T02:17:20.382Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_5i9WFkV9KXgc0UhkGP17zv5a|fc_0c12d400df9be445016ad820afd66487d0a8acef2f9e977ae0", + "function_name": "read", + "arguments": { + "path": "/task/contract.md", + "offset": 1, + "limit": 2000 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_5i9WFkV9KXgc0UhkGP17zv5a|fc_0c12d400df9be445016ad820afd66487d0a8acef2f9e977ae0", + "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": "54d47d17", + "pi_parent_id": "c75320aa", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820ad8cec87d0a164236afc507642", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 3, + "timestamp": "2026-10-21T02:17:22.928Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_NtwpQFwKs7mxKqI1prrpD2yw|fc_0c12d400df9be445016ad820b2650487d0ba72882619a9ff8f", + "function_name": "read", + "arguments": { + "path": "/task/tool-docs.md", + "offset": 1, + "limit": 2000 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_NtwpQFwKs7mxKqI1prrpD2yw|fc_0c12d400df9be445016ad820b2650487d0ba72882619a9ff8f", + "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 + } + } + ] + }, + "metrics": { + "prompt_tokens": 3036, + "completion_tokens": 31, + "cached_tokens": 2665, + "extra": { + "cache_write_tokens": 368, + "reasoning_tokens": 0, + "pi_total_tokens": 3067 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "9755708a", + "pi_parent_id": "a28c8436", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820b109e887d09455d1c121f8d493", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 4, + "timestamp": "2026-10-21T02:17:25.159Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_Dn9B9fE5noYPcd0DuQESWCPi|fc_0c12d400df9be445016ad820b4a43c87d09df8ff2829929416", + "function_name": "read", + "arguments": { + "path": "/task/scoring-policy.json", + "offset": 1, + "limit": 2000 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_Dn9B9fE5noYPcd0DuQESWCPi|fc_0c12d400df9be445016ad820b4a43c87d09df8ff2829929416", + "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 + } + } + ] + }, + "metrics": { + "prompt_tokens": 3190, + "completion_tokens": 31, + "cached_tokens": 3033, + "extra": { + "cache_write_tokens": 154, + "reasoning_tokens": 0, + "pi_total_tokens": 3221 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "afeaa9e5", + "pi_parent_id": "69697523", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820b3b7fc87d0b26065bea82b1829", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 5, + "timestamp": "2026-10-21T02:17:27.319Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_xO4SHmXaSBQiVM7MYVcuQb7U|fc_0c12d400df9be445016ad820b6f2c887d0874824a3f4c57a02", + "function_name": "mcp", + "arguments": { + "server": "benchmark" + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_xO4SHmXaSBQiVM7MYVcuQb7U|fc_0c12d400df9be445016ad820b6f2c887d0874824a3f4c57a02", + "content": "benchmark (4 tools):\n\n- benchmark_get_task_thumbnail - Read the supplied task thumbnail and public brief.\n- benchmark_validate_candidate - Validate a frozen candidate and return NVIDIA...\n- benchmark_submit_asset - End authoring and freeze the submitted 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": 4291, + "completion_tokens": 18, + "cached_tokens": 3187, + "extra": { + "cache_write_tokens": 1101, + "reasoning_tokens": 0, + "pi_total_tokens": 4309 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "b855dcd0", + "pi_parent_id": "0e38648e", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820b5dc2c87d095bb8acf557e9a4f", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 6, + "timestamp": "2026-10-21T02:17:34.670Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "The object is a cordless drill. I\u2019ll model the housing and grip as one rigid assembly, with a removable battery, a sliding trigger and selector, and coaxial rotating clutch and chuck parts. Their joints will use the drill\u2019s front-to-back axis, while the battery will slide along its mounting rails.", + "reasoning_content": "**Considering drill creation in USD**\n\nI\u2019m planning to create a great drill using USD. I wonder if I need to preview the tools. Since there are only four, could I use my own render? Maybe it\u2019s possible to install USD and render it through Python. I\u2019ll need to inspect my environment to see how everything aligns. I\u2019m curious about what adjustments I might need to make along the way! Let's see how this goes.", + "tool_calls": [ + { + "tool_call_id": "call_4a3N81sIXEZ5NdX5c0IwPto6|fc_0c12d400df9be445016ad820bd03b487d088b2c98db8e2bdf1", + "function_name": "bash", + "arguments": { + "command": "ls -la /output && python - <<'PY'\nfrom pxr import Usd, UsdGeom, UsdPhysics\nprint('pxr available')\nPY\nwhich usdrecord || true\nls /task", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_4a3N81sIXEZ5NdX5c0IwPto6|fc_0c12d400df9be445016ad820bd03b487d088b2c98db8e2bdf1", + "content": "total 0\ndrwxrwxrwx 2 actor actor 40 Oct 21 02:17 .\ndrwxr-xr-x 1 root root 40 Oct 21 02:17 ..\npxr available\ncontract.md\ninstruction.md\nscoring-policy.json\nsimready-bench-native-output-policy.ts\nthumbnail.png\ntool-docs.md", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 4383, + "completion_tokens": 172, + "cached_tokens": 4288, + "extra": { + "cache_write_tokens": 92, + "reasoning_tokens": 34, + "pi_total_tokens": 4555 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "578d93ee", + "pi_parent_id": "780e3802", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820b8786887d0b4d2d1a0f2d8cae2", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 7, + "timestamp": "2026-10-21T02:17:54.832Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_nNaoH0MLRf6rNQfj5UAvYffY|fc_0c12d400df9be445016ad820d09c2487d08d9b1caff30a10d9", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nimport importlib.util\nfor a in ['numpy','scipy','trimesh','PIL','manifold3d','pyrender','OpenGL','pxr.UsdLux','pxr.UsdSemantics','pxr.Semantics']:\n try: print(a, bool(importlib.util.find_spec(a)))\n except Exception as e: print(a,e)\nPY\nls /usr/local/bin | grep -E 'usd|render'", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_nNaoH0MLRf6rNQfj5UAvYffY|fc_0c12d400df9be445016ad820d09c2487d08d9b1caff30a10d9", + "content": "numpy True\nscipy False\ntrimesh False\nPIL True\nmanifold3d False\npyrender False\nOpenGL False\npxr.UsdLux True\npxr.UsdSemantics True\npxr.Semantics False\n\n\nCommand exited with code 1", + "extra": { + "tool_name": "bash", + "is_error": true + } + } + ] + }, + "metrics": { + "prompt_tokens": 4644, + "completion_tokens": 758, + "cached_tokens": 4380, + "extra": { + "cache_write_tokens": 261, + "reasoning_tokens": 634, + "pi_total_tokens": 5402 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "f9cce8cc", + "pi_parent_id": "2227be57", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820c019d487d0bbb9ec2bfc59ac51", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 8, + "timestamp": "2026-10-21T02:18:28.470Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "tool_calls": [ + { + "tool_call_id": "call_yi6Ck6lvtk36yaIsfgrjctWG|fc_0c12d400df9be445016ad820f345b887d08cd0754fd016a485", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nfrom pxr import UsdSemantics, Usd, UsdPhysics\nprint(dir(UsdSemantics))\nprint(UsdSemantics.LabelsAPI.__doc__)\nprint(dir(UsdSemantics.LabelsAPI))\nPY", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_yi6Ck6lvtk36yaIsfgrjctWG|fc_0c12d400df9be445016ad820f345b887d08cd0754fd016a485", + "content": "['LabelsAPI', 'LabelsQuery', 'Tokens', '_CanApplyResult', '__MFB_FULL_PACKAGE_NAME', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__']\nNone\n['Apply', 'CanApply', 'ComputeInheritedTaxonomies', 'CreateLabelsAttr', 'Get', 'GetAll', 'GetDirectTaxonomies', 'GetLabelsAttr', 'GetPath', 'GetPrim', 'GetSchemaAttributeNames', 'GetSchemaClassPrimDefinition', 'GetSchemaKind', 'IsAPISchema', 'IsAppliedAPISchema', 'IsConcrete', 'IsMultipleApplyAPISchema', 'IsSemanticsLabelsAPIPath', 'IsTyped', '_GetStaticTfType', '__bool__', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__instance_size__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__']", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 5471, + "completion_tokens": 1042, + "cached_tokens": 4641, + "extra": { + "cache_write_tokens": 827, + "reasoning_tokens": 961, + "pi_total_tokens": 6513 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "4bb4f563", + "pi_parent_id": "2bc2b304", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820d3c6e087d09bc2ff97cadcda4b", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 9, + "timestamp": "2026-10-21T02:18:35.009Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Exploring semantics requirements**\n\nI\u2019m considering whether the validator legacy semantics require attributes defined as semantics. It sounds like I need to apply a semantic type of \"class\" and include everything through custom attributes with an API. I'll look into adding a SemanticsAPI token to the apiSchemas, seeing if the validator recognizes it. First, I need to clarify the requirements by validating them after I figure out how to render locally with external USD imaging tools that aren't available.", + "tool_calls": [ + { + "tool_call_id": "call_H9RMxuKYG8o4sGMjqGlmigf3|fc_0c12d400df9be445016ad820f9b3c487d0ba8a779073a1e55c", + "function_name": "bash", + "arguments": { + "command": "python - <<'PY'\nfrom pxr import Usd\nimport pxr, os\nprint(pxr.__path__)\nfor m in ['UsdImagingGL','UsdAppUtils','UsdRender']:\n try: __import__('pxr.'+m); print(m,'yes')\n except: print(m,'no')\nPY", + "timeout": 200 + } + } + ], + "observation": { + "results": [ + { + "source_call_id": "call_H9RMxuKYG8o4sGMjqGlmigf3|fc_0c12d400df9be445016ad820f9b3c487d0ba8a779073a1e55c", + "content": "['/usr/local/lib/python3.12/dist-packages/pxr']\nUsdImagingGL no\nUsdAppUtils no\nUsdRender yes", + "extra": { + "tool_name": "bash", + "is_error": false + } + } + ] + }, + "metrics": { + "prompt_tokens": 6818, + "completion_tokens": 169, + "cached_tokens": 5468, + "extra": { + "cache_write_tokens": 1347, + "reasoning_tokens": 72, + "pi_total_tokens": 6987 + } + }, + "llm_call_count": 1, + "extra": { + "pi_event_id": "28eb821b", + "pi_parent_id": "4d9b9bdd", + "provider": "harbor-endpoint", + "api": "openai-responses", + "response_id": "resp_0c12d400df9be445016ad820f5853087d0a8b3ab63a543934d", + "response_model": null, + "stop_reason": "toolUse", + "raw_stop_reason": "completed" + } + }, + { + "step_id": 10, + "timestamp": "2026-10-21T02:22:47.579Z", + "source": "agent", + "model_name": "harbor-endpoint/gpt-6.1-sol", + "message": "", + "reasoning_content": "**Considering sample selection**\n\nI need to think about omitting main surface sections if the grid cell matches. It seems important to choose a sufficient number of samples that are dense, including vent edges and theta segments of 128. If certain conditions are met, like skipping faces when mid x is in the slot and abs(cos \u03b8) is greater than .7 along with z bands, that could streamline my approach. I'll make sure to consider these factors carefully!\n**Determining dimensions for components**\n\nI\u2019m analyzing some component measurements. The motor should be at .202, while the gear axis is also .198. All coax must align at .202, and I've got a maximum Z of .24394 at the back with a radius of .04194. For the motor Z, I\u2019ll stick with .202, and for the top, it\u2019s .24394. The front clutch has a radius of .0305, with a top of .2325, and the nose bottom is .183. The battery top measures .074, and grip top is .178.\n**Analyzing grip dimensions**\n\nI\u2019m considering whether the absolute x offset is really that important or if I need to focus on the z dimension, which is .243943. I have the motor cap details lined up, with specific front and rear x positions. The grip silhouette has polygon coordinates that I\u2019ll use in XZ space. I\u2019m also thinking about the grip's angle, wondering if it needs to be lower and more towards the rear. I\u2019ll establish boundaries for both the front and back of the grip to ensure proper attachment.\n\n**Designing grip shapes**\n\nI need to create smooth, rounded shapes for the grip. I want to loft a horizontal grip shape with variable dimensions at various z positions. For example, I'll set the z at .071 with specific x, radius, and y dimensions. I\u2019ve laid out a list for different z values like .08, .105, and others, ensuring each has particular radius settings. The grip will feature a red core, with a black wrap at the front and rear edges. I'll use a rubber shape on the sides with a slight bevel for a better feel.", + "tool_calls": [ + { + "tool_call_id": "call_Dh86hj3lQEiu6IIY4syyvCdy|fc_0c12d400df9be445016ad82134d6b487d0a40abe170c3da9b6", + "function_name": "bash", + "arguments": { + "command": "cat > /tmp/build_asset.py <<'PY'\nimport os, math, random\nimport numpy as np\nfrom PIL import Image, ImageFilter\nfrom pxr import Usd, UsdGeom, UsdShade, UsdPhysics, UsdSemantics, Sdf, Gf, Vt, Kind\nOUT='/output'; os.makedirs(OUT+'/textures',exist_ok=True)\nstage=Usd.Stage.CreateNew(OUT+'/asset.usd')\nUsdGeom.SetStageUpAxis(stage, UsdGeom.Tokens.z); UsdGeom.SetStageMetersPerUnit(stage,1.0)\nstage.SetStartTimeCode(0); stage.SetEndTimeCode(0)\nroot=UsdGeom.Xform.Define(stage,'/Drill'); stage.SetDefaultPrim(root.GetPrim()); root.GetPrim().SetMetadata('kind','component')\nroot.GetPrim().SetCustomDataByKey('assetType','cordless drill driver')\nroot.GetPrim().SetCustomDataByKey('description','Cordless drill with removable slide battery, keyless chuck, torque selector, trigger, direction switch and two-speed selector.')\nroot.GetPrim().SetMetadata('assetInfo',{'name':'Cordless_Drill','version':'1.0'})\nUsdGeom.Scope.Define(stage,'/Drill/Looks'); UsdGeom.Scope.Define(stage,'/Drill/Joints')\ndef semantics(p,label,material=None):\n UsdSemantics.LabelsAPI.Apply(p,'class').CreateLabelsAttr([label])\n UsdSemantics.LabelsAPI.Apply(p,'material').CreateLabelsAttr([material or 'plastic'])\n p.CreateAttribute('semantics:Semantics:semanticType',Sdf.ValueTypeNames.String).Set('class')\n p.CreateAttribute('semantics:Semantics:semanticData',Sdf.ValueTypeNames.String).Set(label)\nsemantics(root.GetPrim(),'cordless_drill','plastic metal rubber')\n# Fine molded polymer texture; non-directional grain with slight abrasion, self-contained maps.\nrng=np.random.default_rng(81)\nnoise=rng.normal(0,1,(512,512)); low=np.array(Image.fromarray(np.uint8(np.clip(noise*20+128,0,255))).filter(ImageFilter.GaussianBlur(4)))/255-.5\nfine=rng.normal(0,1,(512,512))\nmaterial_specs={\n 'Red':((.57,.080,.063),.48,.0,'plastic'),\n 'RedEdge':((.43,.044,.032),.52,.0,'plastic'),\n 'Rubber':((.056,.058,.061),.74,.0,'rubber'),\n 'DarkPlastic':((.092,.094,.099),.57,.0,'plastic'),\n 'RearRubber':((.125,.128,.133),.7,.0,'rubber'),\n 'SilverHousing':((.63,.65,.66),.38,.25,'metal'),\n 'Steel':((.53,.55,.57),.29,.85,'metal'),\n 'DarkSteel':((.20,.215,.23),.38,.75,'metal'),\n 'Black':((.007,.008,.009),.84,.0,'plastic'),\n 'Seam':((.027,.025,.026),.68,.0,'plastic')}\nmats={}\nfor name,(col,rough,metal,category) in material_specs.items():\n mat=UsdShade.Material.Define(stage,'/Drill/Looks/'+name)\n sh=UsdShade.Shader.Define(stage,mat.GetPath().AppendChild('Surface')); sh.CreateIdAttr('UsdPreviewSurface')\n sh.CreateInput('diffuseColor',Sdf.ValueTypeNames.Color3f).Set(Gf.Vec3f(*col))\n sh.CreateInput('roughness',Sdf.ValueTypeNames.Float).Set(rough); sh.CreateInput('metallic',Sdf.ValueTypeNames.Float).Set(metal)\n sh.CreateInput('ior',Sdf.ValueTypeNames.Float).Set(1.48)\n mat.CreateSurfaceOutput().ConnectToSource(sh.ConnectableAPI(),'surface')\n # very subtle roughness micrograin (not an oversized procedural texture)\n arr=np.clip(rough+fine*.024+low*.08,0,1)\n Image.fromarray(np.uint8(arr*255)).save(OUT+'/textures/'+name+'_roughness.png')\n uv=UsdShade.Shader.Define(stage,mat.GetPath().AppendChild('UV')); uv.CreateIdAttr('UsdPrimvarReader_float2'); uv.CreateInput('varname',Sdf.ValueTypeNames.Token).Set('st')\n tex=UsdShade.Shader.Define(stage,mat.GetPath().AppendChild('Grain')); tex.CreateIdAttr('UsdUVTexture'); tex.CreateInput('file',Sdf.ValueTypeNames.Asset).Set(Sdf.AssetPath('textures/'+name+'_roughness.png')); tex.CreateInput('sourceColorSpace',Sdf.ValueTypeNames.Token).Set('raw'); tex.CreateInput('wrapS',Sdf.ValueTypeNames.Token).Set('repeat'); tex.CreateInput('wrapT',Sdf.ValueTypeNames.Token).Set('repeat'); tex.CreateInput('st',Sdf.ValueTypeNames.Float2).ConnectToSource(uv.ConnectableAPI(),'result'); tex.CreateOutput('r',Sdf.ValueTypeNames.Float)\n sh.GetInput('roughness').ConnectToSource(tex.ConnectableAPI(),'r')\n semantics(mat.GetPrim(),category,category); mats[name]=mat\nbodies={}; bodypoints={}\ndef body(name,label,mass,pos=(0,0,0)):\n obj=UsdGeom.Xform.Define(stage,'/Drill/'+name); obj.AddTranslateOp().Set(Gf.Vec3d(*pos)); p=obj.GetPrim()\n rb=UsdPhysics.RigidBodyAPI.Apply(p); rb.CreateRigidBodyEnabledAttr(True); rb.CreateKinematicEnabledAttr(False)\n ma=UsdPhysics.MassAPI.Apply(p); ma.CreateMassAttr(mass)\n semantics(p,label); bodies[name]=p; bodypoints[name]=[]\n return '/Drill/'+name\nH=body('Housing','drill_housing',.78)\nB=body('Battery','rechargeable_battery',.56)\nC=body('TorqueCollar','torque_adjustment_collar',.065,(-.057,0,.202))\nK=body('Chuck','keyless_rotating_chuck',.17,(-.086,0,.202))\nT=body('Trigger','variable_speed_trigger',.008,(-.023,0,.158))\nD=body('DirectionSwitch','forward_reverse_switch',.004,(-.007,0,.175))\nS=body('SpeedSwitch','two_speed_selector',.005,(.011,0,.241))\nL=body('BatteryLatch','battery_release_button',.006,(-.080,0,.041))\n# Geometry helpers. Meshes are closed polygonal solids with authored normals and UVs.\ndef mesh(path,pts,faces,mat,smooth=True,collision=False):\n pts=np.asarray(pts,dtype=float)\n m=UsdGeom.Mesh.Define(stage,path); m.CreatePointsAttr([Gf.Vec3f(*x) for x in pts]); m.CreateFaceVertexCountsAttr([len(f) for f in faces]); m.CreateFaceVertexIndicesAttr([i for f in faces for i in f]); m.CreateSubdivisionSchemeAttr('none')\n m.CreateExtentAttr([Gf.Vec3f(*pts.min(0)),Gf.Vec3f(*pts.max(0))]); m.CreateDoubleSidedAttr(False)\n # explicit smooth vertex normals; flat vertices where desired are split by generators\n acc=np.zeros_like(pts); fn=[]\n for f in faces:\n v=np.cross(pts[f[1]]-pts[f[0]],pts[f[2]]-pts[f[0]])\n fn.append(v/max(np.linalg.norm(v),1e-15))\n for i in f: acc[i]+=v\n acc/=np.maximum(np.linalg.norm(acc,axis=1)[:,None],1e-15)\n if smooth:\n m.CreateNormalsAttr([Gf.Vec3f(*n) for n in acc]); m.SetNormalsInterpolation('vertex')\n else:\n m.CreateNormalsAttr([Gf.Vec3f(*fn[j]) for j,f in enumerate(faces) for i in f]); m.SetNormalsInterpolation('faceVarying')\n st=[]\n for f,n in zip(faces,fn):\n ax=int(np.argmax(np.abs(n))); dims=[a for a in range(3) if a!=ax]\n for i in f: st.append(Gf.Vec2f(float(pts[i,dims[0]]/.045),float(pts[i,dims[1]]/.045)))\n UsdGeom.PrimvarsAPI(m).CreatePrimvar('st',Sdf.ValueTypeNames.TexCoord2fArray,'faceVarying').Set(st)\n UsdShade.MaterialBindingAPI.Apply(m.GetPrim()).Bind(mats[mat])\n # all visual meshes inherit semantics, material labels also on each mesh\n semantics(m.GetPrim(),path.split('/')[2].lower(),material_specs[mat][3])\n bn=path.split('/')[2]\n if not collision: bodypoints[bn].extend(pts.tolist())\n else:\n UsdPhysics.CollisionAPI.Apply(m.GetPrim()).CreateCollisionEnabledAttr(True)\n UsdPhysics.MeshCollisionAPI.Apply(m.GetPrim()).CreateApproximationAttr('convexHull')\n m.CreatePurposeAttr('guide'); m.CreateVisibilityAttr('invisible')\n return m\n\ndef loftx(path,rings,mat,n=96,phase=0,flute=0,flutes=0,center=(0,0), cap=True,skip=None):\n # each ring: x, radiusY, radiusZ. All coordinates local to the body.\n pts=[]\n for x,ry,rz in rings:\n for j in range(n):\n t=phase+2*math.pi*j/n; mod=1\n if flutes: mod=1+flute*(.5+.5*math.cos(flutes*t))\n pts.append((x,center[0]+ry*math.cos(t)*mod,center[1]+rz*math.sin(t)*mod))\n faces=[]\n for i in range(len(rings)-1):\n for j in range(n):\n if skip and skip((rings[i][0]+rings[i+1][0])/2,phase+2*math.pi*(j+.5)/n): continue\n k=(j+1)%n; faces.append([i*n+j,i*n+k,(i+1)*n+k,(i+1)*n+j])\n # reversed? theta path y=cos,z=sin. at theta0 along x cross ring normal +y with ring then x -> +y. above is correct\n if cap:\n # separate vertices for flat end caps\n for idx,reverse in [(0,True),(len(rings)-1,False)]:\n ids=[]\n for j in range(n): ids.append(len(pts)); pts.append(pts[idx*n+j])\n faces.append(ids[::-1] if reverse else ids)\n return mesh(path,pts,faces,mat)\n\ndef rounded(poly,r=.12,steps=4):\n # corner cut, quadratic softened polygon preserving overall outline\n a=np.array(poly,float); out=[]\n for i,p in enumerate(a):\n p0=p+(a[i-1]-p)*r; p1=p+(a[(i+1)%len(a)]-p)*r\n for t in np.linspace(0,1,steps,endpoint=False): out.append((1-t)**2*p0+2*(1-t)*t*p+t*t*p1)\n return np.array(out)\n\ndef extrude_profile(path,poly,axis,rings,mat,rounding=.1,smooth=True):\n # polygon coordinates in dimensions other than extrusion axis; rings=(axis position, profile scale)\n poly=rounded(poly,rounding,4) if rounding else np.array(poly,float)\n # make polygon CCW\n area=sum(poly[i,0]*poly[(i+1)%len(poly),1]-poly[(i+1)%len(poly),0]*poly[i,1] for i in range(len(poly)))\n if area<0: poly=poly[::-1]\n dims=[d for d in range(3) if d!=axis]; center=poly.mean(0); n=len(poly); pts=[]\n for val,scale in rings:\n for p in poly:\n pp=center+(p-center)*scale; q=[0.,0.,0.]; q[axis]=val; q[dims[0]]=pp[0];q[dims[1]]=pp[1];pts.append(q)\n faces=[]\n for k in range(len(rings)-1):\n for i in range(n):\n # cross winding swaps for axis y\n f=[k*n+i,k*n+(i+1)%n,(k+1)*n+(i+1)%n,(k+1)*n+i]\n faces.append(f[::-1] if axis!=1 else f)\n # cap lower normal along -axis; polygon cross in remaining dimensions + if axis not y\n for k,upper in [(0,False),(len(rings)-1,True)]:\n ids=[]\n for i in range(n): ids.append(len(pts));pts.append(pts[k*n+i])\n if upper == (axis==1): ids=ids[::-1]\n faces.append(ids)\n return mesh(path,pts,faces,mat,smooth)\n\ndef tube(path,line,r,mat,n=8):\n line=np.array(line,float); pts=[]\n for i,p in enumerate(line):\n d=line[min(i+1,len(line)-1)]-line[max(0,i-1)];d/=max(np.linalg.norm(d),1e-9)\n u=np.cross(d,[0,1,0] if abs(d[1])<.95 else [1,0,0]);u/=np.linalg.norm(u);v=np.cross(d,u)\n for j in range(n):pts.append(p+r*(u*math.cos(j*2*math.pi/n)+v*math.sin(j*2*math.pi/n)))\n fs=[]\n for i in range(len(line)-1):\n for j in range(n):fs.append([i*n+j,i*n+(j+1)%n,(i+1)*n+(j+1)%n,(i+1)*n+j])\n fs.extend([list(range(n))[::-1],list(range((len(line)-1)*n,len(line)*n))]);return mesh(path,pts,fs,mat)\n\ndef cylinder(path,center,r,length,axis,mat,n=48):\n # use x loft and transform points for other axes\n rings=[(-length/2,r,r),(length/2,r,r)]\n x=loftx(path,rings,mat,n)\n ps=np.array(x.GetPointsAttr().Get())\n if axis==1: ps=ps[:,[1,0,2]];ps[:,2]*=-1\n elif axis==2: ps=ps[:,[1,2,0]]\n ps+=np.array(center); x.GetPointsAttr().Set([Gf.Vec3f(*p) for p in ps]);x.GetExtentAttr().Set([Gf.Vec3f(*ps.min(0)),Gf.Vec3f(*ps.max(0))])\n # rotate authored normals too\n ns=np.array(x.GetNormalsAttr().Get())\n if axis==1:ns=ns[:,[1,0,2]];ns[:,2]*=-1\n elif axis==2:ns=ns[:,[1,2,0]]\n x.GetNormalsAttr().Set([Gf.Vec3f(*p) for p in ns]);return x\n\n# Main motor skin, with real perforations over a dark internal liner.\nzc=.202\nxs=[-.027,-.024,-.019,-.01,.008,.025,.039,.044,.047,.050,.054,.057,.061,.064,.068,.071,.073]\ndef radius(x):\n return np.interp(x,[-.027,-.019,.025,.060,.073],[.0307,.0358,.038,.0377,.034])\ndef vents(x,t):\n col=(.047