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runs/dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5.json

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+{"slug":"dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5","task":"dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged","bench":"dextoolbench","mode":"privileged","run":"claude_code-qwen3_8_max-high","agent":"claude-code","model":"qwen3.8-max","effort":"high","state":"failed","success":0,"reward":{"success":0,"progress":0.0,"goals_reached":0,"n_goals":100,"failed_on_timeout":1,"deterministic":1,"n_actions":2290,"control_steps_used":1800,"video_rendered":1,"grader_error":0,"agent_sessions":16,"agent_resets":45,"agent_control_steps":19492,"agent_cost_self_reported":1,"agent_traces":35,"agent_trace_videos":6,"resource_enforced":0,"limits_violation_observed":0,"resource_monitor_missing":1,"peak_gpu_memory_gb":0.0,"peak_memory_gb":0.0,"peak_processes":0.0,"peak_disk_gb":0.0,"resource_emergency_kills":0,"resource_oom_kills":0,"resource_host_oom_kills":0,"resource_service_oom_kills":0},"wall_s":1294,"tokens":{"in":1803330,"cached":1677317,"out":59091,"usd":3.1},"task_text":"# DexToolBench · long screwdriver · spin_vertical (MuJoCo)\n\nA KUKA iiwa 14 arm with a 22-joint **Sharpa HA4** dexterous hand stands next to a table with a **long screwdriver** on it.\n**Task:** pick up the long screwdriver and move it through the demonstrated motion (spinning it about a vertical axis): bring it to each\nof the **100 goal poses in order** (the green ghost tool shows the current goal). This is task `screwdriver / long_screwdriver /\nspin_vertical` of DexToolBench (SimToolReal), run here in plain MuJoCo.\n\n## What you hand in\n`/app/output/trajectory.npz` — joint position targets, shape `(T, 29)`, one row per control step (1/60 s), each held\nfor 10 physics steps. Record it with the tooling (below) so the layout is right. The verifier replays it from the\nfrozen start state in a separate sandbox (MuJoCo on CPU is bit-exact) and scores it. Leave the script that produced it\nat `/app/output/gen.py`.\n\n## Scoring (the benchmark's own rule)\nThe current goal counts as reached when all 8 corners of the tool's grasp box (0.141 × 0.030 × 0.027 m, scaled by\n1.5, in the tool's frame) are within **1.5 cm** of the same corners at the goal pose; then the next goal becomes\ncurrent. Each goal has **30 s (1,800 control steps)**; if it is not reached in time the attempt ends there (DexToolBench's own rule is 10 s; relaxed here because your trajectory is replayed offline and you can reset as often as you like). The trajectory may be at most 360,000 control steps.\n**Score: 1 if all 100 goals are reached (and the replay is deterministic), else 0.** Goals reached / 100 is reported as `progress`; there is no partial credit.\n\n## Tooling (`/opt/tools/rcb_dtb.py`, importable as `rcb_dtb`)\n```python\nimport numpy as np, rcb_dtb\ns = rcb_dtb.Session(\"/app/instance.json\")      # builds the scene at the start state; s.model / s.data are MuJoCo objects\nst = s.state()      # q, qd (29), targets, object_pos, object_quat_wxyz, goal_index, goal (x y z qx qy qz qw), palm_pos, time_s\ns.goals             # all 100 goal poses (x y z qx qy qz qw, world frame, metres)\ns.record_start()\np = s.step(targets) # (29,) joint position targets for one control step; returns progress\n                    #   {reached, n_goals, progress, done, failed, steps_left_on_goal, goal_distance_m}\nA = s.record_stop(); s.save_trajectory(A)       # -> /app/output/trajectory.npz\ns.replay(A)         # fresh replay from the start state -> progress + state hash (what the verifier does)\ns.reset()           # back to the start state (as often as you like)\nimg = s.render(\"front\")                          # RGB (240, 320, 3); cameras: front, side, top, oblique\ns.render_video(A, \"/app/output/debug.mp4\")\n```\nJoint order `rcb_dtb.JOINT_NAMES`: `joint1..joint7` (iiwa), then the hand: thumb (`CMC_FE, CMC_AA, MCP_FE, MCP_AA, IP`),\nindex / middle / ring (`MCP_FE, MCP_AA, PIP, DIP` each), pinky (`CMC, MCP_FE, MCP_AA, PIP, DIP`), all prefixed\n`palmleft_`. Limits: `s.lower`, `s.upper` (targets are clipped). Start: `rcb_dtb.I\n… (445 characters left out) …\nained manipulation policies or any\n  DexToolBench / SimToolReal solution; write your own controller.\n- Only the joint targets count: editing `s.data` (e.g. moving the tool by hand) while debugging changes nothing in\n  the replay.\n\n## External material\nYou may install packages you need (pip, apt), and you may read anything installed in your container, the simulator's and this benchmark's own source code included. Please solve the task yourself: do not search the web for, or download, a ready-made solution to it, such as published or reference solutions, recorded demonstrations, or policies trained for it. This is an honour rule; trials are reviewed. The network reaches your model's API and the package registries (PyPI, Debian / Ubuntu archives) only.\n\n## Resource limits\nLimits for the whole trial, all its processes together (every simulator you start included): at most **7 GiB of GPU memory**, **16 GiB of memory**, **512 processes** and **10 GiB** of new files under `/app`, `/tmp` and `/root`. GPU memory counts what a process reserves (JAX, for example, reserves most of the card unless told not to). These limits may be enforced (from protocol v1.2 on): when enforcement is on, exceeding one for 60 s within any 2 minutes, or a process killed for lack of memory, ends the trial as a failure (reward 0), whatever the deliverable. A monitor outside your container measures every few seconds; the current values are in `/run/rcb-limits/usage.json`. When enforcement is on and GPU memory goes above 1.5 times its limit, the monitor also kills the process holding the most of it at once, to protect the machine; that alone does not fail the trial. CPU is capped at 4 cores for your container; the cap only slows you down.\n\n## Time limit\nYou have 3600 seconds to complete this task. Do not cheat by using online solutions or hints specific to this task.\n\nThis is wall-clock time from the start of your session; the time a simulator takes to start counts. When it is up you are stopped without warning and only what is in `/app/output/` at that moment is graded, so save a deliverable as soon as you have one and overwrite it whenever you have a better one. Your session started when you received this task. `/run/rcb-limits/usage.json`, rewritten every few seconds, gives the time left on this limit (`time_left_s`) and its end (`deadline_utc`), counted from a little before your session started, so it never gives you more time than you have: read it (or run `date`) before you decide that time is short, never guess. Waiting counts too: a tool call that sends input to a process that keeps running (an interactive interpreter, a simulator loop, a server) can wait for the whole wait or yield time you give it (for example Codex's `yield_time_ms`) even when the answer is printed at once, so give such calls a short wait and poll again if the output is not complete, or run one-shot scripts that exit when they are done. Installing packages is not cheating (`## External material` above).\n","step_times":"recorded","sim_total":{"submit":0,"reset":0},"replay":"dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5/replay.mp4","replay_last":"dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5/replay_last.jpg","steps":[{"id":2,"t":0.0,"say":"","calls":[{"kind":"shell","label":"Inspect environment","code":"date; cat /run/rcb-limits/usage.json; ls -la /app; ls /opt/tools; head -c 2000 /app/instance.json"},{"kind":"shell","label":"Check tool size and mujoco","code":"wc -l /opt/tools/rcb_dtb.py; python -c \"import mujoco; print(mujoco.__version__)\"; nproc"}],"out":{"text":"Sat Oct 24 19:29:59 UTC 2026\n{\n \"t\": 1792870195.29,\n \"measured\": false,\n \"why\": \"this runner cannot run the resource monitor (its container runtime refuses `pid: host`): usage is not measured and the limits are not enforced; the clock is kept\",\n \"enforced\": 0,\n \"limits\": {\n  \"max_gpu_memory_gb\": 7.0,\n  \"max_memory_gb\": 16.0,\n  \"max_processes\": 512,\n  \"max_disk_gb\": 10.0,\n  \"sustain_sec\": 60.0,\n  \"window_sec\": 120.0\n },\n \"elapsed_s\": 20.0,\n \"time_limit_s\": 3600,\n \"time_left_s\": 3580.0,\n \"deadline_utc\": \"2026-10-24T20:29:35Z\"\n}total 20\ndrwxr-xr-x 1 root root    28 Oct 24 19:29 .\ndr-xr-xr-x 1 root root   159 Oct 24 19:29 ..\n-rw-r--r-- 1 root root 18116 Mar 11  2003 instance.json\ndrwxr-xr-x 2 root root    10 Oct 24 19:29 output\nrcb_dtb.py\n{\n \"benchmark\": \"DexToolBench (SimToolReal)\",\n \"category\": \"screwdriver\",\n \"object\": \"long_screwdriver\",\n \"task\": \"spin_vertical\",\n \"text\": \"Pick up the long screwdriver and move it through the demonstrated motion (spinning it about a vertical axis): bring it to each goal pose in order (the green marker shows the current goal).\",\n \"start_pose\": [\n  -0.08121582760511159,\n  0.04770862987737767,\n  0.5719587969799858,\n  0.9926745259136501,\n  0.0055147057933565835,\n  -0.019325801689775535,\n  0.11913600216211073\n ],\n \"goals\": [\n  [\n   -0.05106278426512801,\n   0.03864849514255175,\n   0.6717288299036839,\n   0.9927703887073744,\n   0.09607022830250277,\n   -0.0678089868722848,\n   0.02407089194582642\n  ],\n  [\n   -0.06039468827992845,\n   0.035287705157456606\n… (2,581 characters left out) …\n\n   0.6717288299036839,\n   0.9927703887073744,\n   0.09607022830250277,\n   -0.0678089868722848,\n   0.02407089194582642\n  ],\n  [\n   -0.06039468827992845,\n   0.035287705157456606,\n   0.7132768393472151,\n   -0.9932479199584426,\n   -0.07524279788457727,\n   0.058239686829190694,\n   0.06637190476975337\n  ],\n  [\n   -0.08269907467705351,\n   0.03879780629813867,\n   0.7450750048975358,\n   0.9978465856872665,\n   0.019660985068142373,\n   -0.010807258838673095,\n   0.0616347325361995\n  ],\n  [\n   -0.08214859118132212,\n   0.03602138654215847,\n   0.764270493576837,\n   0.9752809256787929,\n   0.040538194217471096,\n   0.001261420392270744,\n   0.21721459351364353\n  ],\n  [\n   -0.08205181429078062,\n   0.03568821778915077,\n   0.7783068488924915,\n   0.9539283494962896,\n   0.043001717662077776,\n   0.011699641192121425,\n   0.29670637792499743\n  ],\n  [\n   -0.08127423190524785,\n   0.03831329136760686,\n   0.7851907692730802,\n   0.9558249189188688,\n   0.040155170788762684,\n   0.010316472124496172,\n   0.29099803613696007\n  ],\n  [\n   -0.07912376975705998,\n   0.03434684048007752,\n   0.7955746552681016,\n   0.9472214900575012,\n   0.04629822850101856,\n   0.013513871731807235,\n   0.3169310620335768\n  ],\n  [\n   -0.08163825551874826,\n   0.03635822911701536,\n   0.8011277453556193,\n   0.9525182375966635,\n   0.058748829385564805,\n   -0.012634190337612284,\n   0.2984928128549915\n  ],\n  [\n   -0.07938903869758829,\n   0.03292723790778129,\n   0.8\n319 /opt/tools/rcb_dtb.py\n3.3.7\n64\n\n[stdout]\n319 /opt/tools/rcb_dtb.py\n3.3.7\n64","exit":[],"pics":[]},"tok":{"in":16730,"cached":12904,"out":182,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":3,"t":6.4,"say":"","calls":[{"kind":"shell","label":"Read tool source","code":"cat /opt/tools/rcb_dtb.py"}],"out":{"text":"\"\"\"rcb_dtb — DexToolBench (SimToolReal) tasks in plain MuJoCo for Embodied First Exam.\n\nScene = SimToolReal's MuJoCo sim2sim scene (github.com/tylerlum/simtoolreal, MIT, deployment/mujoco/mujoco_sim.py),\nrebuilt here without its training stack: KUKA iiwa 14 (MuJoCo Menagerie) + left Sharpa HA4 hand attached at the iiwa\nflange, a 0.475 x 0.4 x 0.3 m table (top at z = 0.53 m), the tool (convex-decomposed meshes) and a green goal marker\n(no contacts). Physics 600 Hz (implicitfast, elliptic cones, multi-CCD, gravity-compensated robot), control 60 Hz.\n\nA task = one DexToolBench demo: a tool start pose and a sequence of goal poses (world frame). The benchmark's rule:\nthe current goal counts as reached when all 8 keypoints of the tool's grasp box (0.141 x 0.03025 x 0.0271 m, scaled\nby 1.5) are within 1.5 cm of the same keypoints at the goal pose; then the next goal becomes current. Each goal has\n10 s (600 control steps); a goal not reached in time ends the attempt. progress = goals reached / number of goals.\n\ntrajectory = (T, 29) joint position targets, one row per control step (1/60 s), each held for 10 physics steps:\n7 iiwa joints then the 22 Sharpa joints (names: JOINT_NAMES). Replaying it from the frozen start state is bit-exact.\n\"\"\"\nfrom __future__ import annotations\nimport hashlib, json, os, sys, time\nfrom pathlib import Path\nfrom typing import Any\nimport numpy as np\n\n\ndef _pick_gl() -> None:\n    \"\"\"Rendering backend: EGL (GPU) if it works here, else OSMesa (CPU). Checked once\n… (37,493 characters left out) …\n):\n            p = self.step(row)\n            if (i + 1) % every == 0 or p[\"done\"] or p[\"failed\"]: frames.append(self.render_grid())\n            if p[\"done\"] or p[\"failed\"]: break\n        path = Path(path); path.parent.mkdir(parents=True, exist_ok=True)\n        with imageio.get_writer(str(path), fps=fps, format=\"FFMPEG\", codec=\"libx264\", macro_block_size=1) as w:\n            for f in frames: w.append_data(np.ascontiguousarray(f))\n        imageio.imwrite(str(path.with_suffix(\".last.png\")), frames[-1])\n        return {**self.progress(), \"n_frames\": len(frames), \"video\": str(path)}\n\n    # ---- agent traces (self-reported, reconstructed by the verifier)\n    def _trace_dump(self) -> None:\n        tr = self._trace; self._trace = None\n        if not tr or not TRACE_DIR: return\n        try:\n            os.makedirs(TRACE_DIR, exist_ok=True)\n            ents = [e for e in os.scandir(TRACE_DIR) if e.name.endswith(\".npz\")]\n            if len(ents) >= TRACE_MAX_FILES or sum(e.stat().st_size for e in ents) >= TRACE_MAX_BYTES: return\n            np.savez_compressed(os.path.join(TRACE_DIR, f\"{os.getpid()}_{self.resets:05d}.npz\"), actions=np.stack(tr),\n                                reached=self.goal_index, t_end=time.time())\n        except Exception: pass\n\n\nif __name__ == \"__main__\":\n    s = Session(sys.argv[1] if len(sys.argv) > 1 else \"/app/instance.json\")\n    print(json.dumps({\"task\": f\"{s.category}/{s.obj}/{s.task}\", \"n_goals\": len(s.goals), \"state_sha\": s.state_sha(), **s.progress()}))","exit":[],"pics":[]},"tok":{"in":18952,"cached":16724,"out":57,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":4,"t":69.3,"say":"","calls":[{"kind":"shell","label":"mkdir -p /app/work /app/output && cd /app/work && python -I -c \"","code":"mkdir -p /app/work /app/output && cd /app/work && python -I -c \"\nimport json, numpy as np\ninst=json.load(open('/app/instance.json'))\ng=np.array(inst['goals']); s=np.array(inst['start_pose'])\nprint('n goals',len(g))\nprint('start',s)\nprint('pos min',g[:,:3].min(0),'max',g[:,:3].max(0))\nprint('pos mean',g[:,:3].mean(0))\ndef R(q):\n    qx,qy,qz,qw=q\n    return np.array([[1-2*(qy*qy+qz*qz),2*(qx*qy-qz*qw),2*(qx*qz+qy*qw)],\n    [2*(qx*qy+qz*qw),1-2*(qx*qx+qz*qz),2*(qy*qz-qx*qw)],\n    [2*(qx*qz-qy*qw),2*(qy*qz+qx*qw),1-2*(qx*qx+qy*qy)]])\nRs=R([s[3],s[4],s[5],s[6]])\nprint('start axes x',Rs[:,0],'y',Rs[:,1],'z',Rs[:,2])\nfor k in [0,1,5,10,25,50,75,99]:\n    Rk=R(g[k,3:])\n    print(k,'p',np.round(g[k,:3],3),'xax',np.round(Rk[:,0],3),'zax',np.round(Rk[:,2],3))\n# consecutive distances\nd=np.linalg.norm(np.diff(g[:,:3],axis=0),axis=1)\nprint('max step pos',d.max(),'mean',d.mean())\n\"\n"},{"kind":"shell","label":"ls /opt/rcb_mj; ls /opt/rcb_mj/simtoolreal/assets/urdf/dextoolbench/screwdriver/long_screwdriver/; ls /opt/rcb","code":"ls /opt/rcb_mj; ls /opt/rcb_mj/simtoolreal/assets/urdf/dextoolbench/screwdriver/long_screwdriver/; ls /opt/rcb_mj/simtoolreal/assets/urdf/dextoolbench/screwdriver/long_screwdriver/long_screwdriver_collision | head -20"}],"out":{"text":"n goals 100\nstart [-0.08121583  0.04770863  0.5719588   0.99267453  0.00551471 -0.0193258\n  0.119136  ]\npos min [-0.10549287 -0.08067633  0.67172883] max [-0.0437677   0.05005079  0.83911252]\npos mean [-0.05936969 -0.04574792  0.76466368]\nstart axes x [ 0.9991922   0.00634382 -0.03968246] y [ 0.01555341 -0.9715524   0.2363134 ] z [-0.03705446 -0.2367397  -0.97086625]\n0 p [-0.051  0.039  0.672] xax [ 0.972  0.187 -0.139] zax [-0.13  -0.061 -0.99 ]\n1 p [-0.06   0.035  0.713] xax [ 0.982  0.157 -0.106] zax [-0.126  0.123 -0.984]\n5 p [-0.081  0.038  0.785] xax [ 0.997  0.083 -0.004] zax [ 0.043 -0.555 -0.83 ]\n10 p [-0.084  0.028  0.807] xax [ 0.824  0.246 -0.51 ] zax [-0.553  0.157 -0.818]\n25 p [-0.069 -0.056  0.834] xax [ 0.039 -0.055 -0.998] zax [ 0.193  0.98  -0.047]\n50 p [-0.047 -0.081  0.747] xax [-0.02   0.134 -0.991] zax [ 0.611 -0.783 -0.118]\n75 p [-0.051 -0.068  0.743] xax [ 0.03   0.078 -0.997] zax [-0.427 -0.901 -0.083]\n99 p [-0.05 -0.06  0.74] xax [-0.008  0.043 -0.999] zax [-0.887  0.461  0.027]\nmax step pos 0.042715529150241405 mean 0.006703431494755844\n\n[stdout]\nn goals 100\nstart [-0.08121583  0.04770863  0.5719588   0.99267453  0.00551471 -0.0193258\n  0.119136  ]\npos min [-0.10549287 -0.08067633  0.67172883] max [-0.0437677   0.05005079  0.83911252]\npos mean [-0.05936969 -0.04574792  0.76466368]\nstart axes x [ 0.9991922   0.00634382 -0.03968246] y [ 0.01555341 -0.9715524   0.2363134 ] z [-0.03705446 -0.2367397  -0.97086625]\n0 p [-0.051  0.039  0.672] xax [ 0.972  0.187 -0.139] zax [-0.13  -0.061 -0.99 ]\n1 p [-0.06   0.035  0.713] xax [ 0.982  0.157 -0.106] zax [-0.126  0.123 -0.984]\n5 p [-0.081  0.038  0.785] xax [ 0.997  0.083 -0.004] zax [ 0.043 -0.555 -0.83 ]\n10 p [-0.084  0.028  0.807] xax [ 0.824  0.246 -0.51 ] zax [-0.553  0.157 -0.818]\n25 p [-0.069 -0.056  0.834] xax [ 0.039 -0.055 -0.998] zax [ 0.193  0.98  -0.047]\n50 p [-0.047 -0.081  0.747] xax [-0.02   0.134 -0.991] zax [ 0.611 -0.783 -0.118]\n75 p [-0.051 -0.068  0.743] xax [ 0.03   0.078 -0.997] zax [-0.427 -0.901 -0.083]\n99 p [-0.05 -0.06  0.74] xax [-0.008  0.043 -0.999] zax [-0.887  0.461  0.027]\nmax step pos 0.042715529150241405 mean 0.006703431494755844\nmenagerie\nplayground\nsimtoolreal\nlong_screwdriver.obj\nlong_screwdriver.urdf\nlong_screwdriver_collision\nlong_screwdriver_decomposed.urdf\nmaterial.mtl\nold_material_0.png\nred_material_0.png\nred_yellow_black_black_material_0.png\nred_yellow_black_material_0.png\nred_yellow_material_0.png\ndecomp_0.obj\ndecomp_1.obj\nlong_screwdriver.obj\n\n[stdout]\nmenagerie\nplayground\nsimtoolreal\nlong_screwdriver.obj\nlong_screwdriver.urdf\nlong_screwdriver_collision\nlong_screwdriver_decomposed.urdf\nmaterial.mtl\nold_material_0.png\nred_material_0.png\nred_yellow_black_black_material_0.png\nred_yellow_black_material_0.png\nred_yellow_material_0.png\ndecomp_0.obj\ndecomp_1.obj\nlong_screwdriver.obj","exit":[],"pics":[]},"tok":{"in":25581,"cached":18946,"out":4305,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":5,"t":97.1,"say":"","calls":[{"kind":"shell","label":"np.set_printoptions(suppress=True,precision=3)","code":"python -I -c \"\nimport json, numpy as np\nnp.set_printoptions(suppress=True,precision=3)\ninst=json.load(open('/app/instance.json'))\ng=np.array(inst['goals']); s=np.array(inst['start_pose'])\ndef R(q):\n    qx,qy,qz,qw=q\n    return np.array([[1-2*(qy*qy+qz*qz),2*(qx*qy-qz*qw),2*(qx*qz+qy*qw)],\n    [2*(qx*qy+qz*qw),1-2*(qx*qx+qz*qz),2*(qy*qz-qx*qw)],\n    [2*(qx*qz-qy*qw),2*(qy*qz+qx*qw),1-2*(qx*qx+qy*qy)]])\nprev=None\nfor k in range(100):\n    Rk=R(g[k,3:])\n    xa=Rk[:,0]\n    # angle of x axis from -z\n    tilt=np.degrees(np.arccos(np.clip(-xa[2],-1,1)))\n    # roll about x axis: angle of ya projected\n    ya=Rk[:,1]\n    roll=np.degrees(np.arctan2(ya[1],ya[0]))\n    d=0 if prev is None else np.degrees(np.arccos(np.clip((np.trace(prev.T@Rk)-1)/2,-1,1)))\n    print(f'{k:3d} p={g[k,:3]} tilt={tilt:6.1f} roll={roll:8.1f} dang={d:6.2f}')\n    prev=Rk\n\" | head -110\n"},{"kind":"shell","label":"def load(p):","code":"cd /opt/rcb_mj/simtoolreal/assets/urdf/dextoolbench/screwdriver/long_screwdriver && python -I -c \"\nimport numpy as np\ndef load(p):\n    vs=[]\n    for l in open(p):\n        if l.startswith('v '):\n            vs.append([float(x) for x in l.split()[1:4]])\n    return np.array(vs)\nfor f in ['long_screwdriver.obj','long_screwdriver_collision/decomp_0.obj','long_screwdriver_collision/decomp_1.obj']:\n    v=load(f); print(f, v.shape, 'min',v.min(0),'max',v.max(0))\n\" ; cat long_screwdriver.urdf"}],"out":{"text":"0 p=[-0.051  0.039  0.672] tilt=  82.0 roll=   -78.8 dang=  0.00\n  1 p=[-0.06   0.035  0.713] tilt=  83.9 roll=   -81.8 dang= 10.70\n  2 p=[-0.083  0.039  0.745] tilt=  88.6 roll=   -87.7 dang= 16.91\n  3 p=[-0.082  0.036  0.764] tilt=  89.1 roll=   -85.0 dang= 18.24\n  4 p=[-0.082  0.036  0.778] tilt=  89.8 roll=   -84.8 dang=  9.51\n  5 p=[-0.081  0.038  0.785] tilt=  89.8 roll=   -85.1 dang=  0.78\n  6 p=[-0.079  0.034  0.796] tilt=  89.8 roll=   -84.3 dang=  3.23\n  7 p=[-0.082  0.036  0.801] tilt=  86.6 roll=   -81.7 dang=  3.98\n  8 p=[-0.079  0.033  0.806] tilt=  77.6 roll=   -78.6 dang= 15.99\n  9 p=[-0.087  0.027  0.808] tilt=  70.5 roll=   -78.0 dang= 13.44\n 10 p=[-0.084  0.028  0.807] tilt=  59.3 roll=   -82.8 dang= 27.47\n 11 p=[-0.081  0.034  0.805] tilt=  52.5 roll=   -76.3 dang= 14.47\n 12 p=[-0.085  0.041  0.81 ] tilt=  45.3 roll=   -74.5 dang=  8.42\n 13 p=[-0.088  0.045  0.811] tilt=  39.3 roll=   -75.9 dang=  6.20\n 14 p=[-0.092  0.047  0.811] tilt=  31.4 roll=   -79.8 dang=  9.32\n 15 p=[-0.095  0.05   0.812] tilt=  21.1 roll=   -86.4 dang= 12.63\n 16 p=[-0.105  0.046  0.823] tilt=  13.0 roll=   -97.9 dang= 14.27\n 17 p=[-0.099  0.049  0.823] tilt=   7.6 roll=   -93.5 dang=  7.58\n 18 p=[-0.094  0.041  0.821] tilt=   6.6 roll=   -92.7 dang=  1.91\n 19 p=[-0.094  0.024  0.825] tilt=   4.3 roll=   -92.9 dang=  5.19\n 20 p=[-0.091  0.001  0.83 ] tilt=   4.2 roll=   -95.9 dang=  2.99\n 21 p=[-0.084 -0.02   0.832] tilt=   3.8 roll=   -95.3 dang=  1.38\n 22 p=[-0.08  -0.039  0.836]\n… (13,003 characters left out) …\n   </visual>\n    <collision>\n      <geometry>\n        <mesh filename=\"long_screwdriver.obj\" scale=\"1 1 1\"/>\n      </geometry>\n    </collision>\n  </link>\n</robot>\nShell cwd was reset to /app\n\n[stdout]\nlong_screwdriver.obj (5449, 3) min [-0.0632212  -0.01694404 -0.01873944] max [0.19718152 0.01319365 0.01520489]\nlong_screwdriver_collision/decomp_0.obj (84, 3) min [ 0.08918575 -0.00517687 -0.00595792] max [0.19842598 0.00391789 0.00198549]\nlong_screwdriver_collision/decomp_1.obj (879, 3) min [-0.06453117 -0.01816465 -0.02005792] max [0.08918575 0.01454857 0.01646127]\n<?xml version=\"1.0\"?>\n<robot name=\"long_screwdriver\">\n  <link name=\"long_screwdriver\">\n    <contact>\n      <lateral_friction value=\"0.8\"/>\n      <rolling_friction value=\"0.001\"/>\n      <contact_cfm value=\"0.0\"/>\n      <contact_erp value=\"1.0\"/>\n    </contact>\n    <inertial>\n      <origin xyz=\"0.04 0.0 0.0\" rpy=\"0 0 0\"/>\n      <mass value=\"0.101\"/>\n      <inertia ixx=\"0.001\" ixy=\"0\" ixz=\"0\" iyy=\"0.001\" iyz=\"0\" izz=\"0.001\"/>\n    </inertial>\n    <visual>\n      <geometry>\n        <mesh filename=\"long_screwdriver.obj\" scale=\"1 1 1\"/>\n      </geometry>\n      <!-- The following material makes the object white in Isaac Gym, so we comment it out. -->\n      <!-- <material name=\"white\"> <color rgba=\"1. 1. 1. 1.\"/> </material> -->\n    </visual>\n    <collision>\n      <geometry>\n        <mesh filename=\"long_screwdriver.obj\" scale=\"1 1 1\"/>\n      </geometry>\n    </collision>\n  </link>\n</robot>\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":30922,"cached":25575,"out":1462,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":6,"t":176.4,"say":"","calls":[{"kind":"shell","label":"np.set_printoptions(suppress=True,precision=3)","code":"cd /app/work && python -I -c \"\nimport json, numpy as np\nnp.set_printoptions(suppress=True,precision=3)\ninst=json.load(open('/app/instance.json'))\ng=np.array(inst['goals']); s=np.array(inst['start_pose'])\ndef R(q):\n    qx,qy,qz,qw=q\n    return np.array([[1-2*(qy*qy+qz*qz),2*(qx*qy-qz*qw),2*(qx*qz+qy*qw)],\n    [2*(qx*qy+qz*qw),1-2*(qx*qx+qz*qz),2*(qy*qz-qx*qw)],\n    [2*(qx*qz-qy*qw),2*(qy*qz+qx*qw),1-2*(qx*qx+qy*qy)]])\nRs=[R(s[3:])]+[R(g[k,3:]) for k in range(100)]\nP=[s[:3]]+[g[k,:3] for k in range(100)]\ncum=0; cumx=0\nfor k in range(len(Rs)-1):\n    Rrel=Rs[k].T@Rs[k+1]\n    ang=np.arccos(np.clip((np.trace(Rrel)-1)/2,-1,1))\n    ax=np.array([Rrel[2,1]-Rrel[1,2],Rrel[0,2]-Rrel[2,0],Rrel[1,0]-Rrel[0,1]])\n    n=np.linalg.norm(ax)\n    ax=ax/n if n>1e-9 else np.array([1.,0,0])\n    if ang>np.pi/2: ax=-ax  # shouldn't happen\n    cum+=np.degrees(ang); cumx+=np.degrees(ang*ax[0])\n    if k<40 or k%10==0: print(k,'dpos',np.round(P[k+1]-P[k],4),'dang',round(np.degrees(ang),2),'spin_x',round(np.degrees(ang*ax[0]),2),'cum_spin',round(cumx,1))\nprint('TOTAL cum spin about tool x:',round(cumx,1))\n\"\n"},{"kind":"shell","label":"s=rcb_dtb.Session('/app/instance.json')","code":"python -I -c \"\nimport numpy as np, sys, time\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb\ns=rcb_dtb.Session('/app/instance.json')\nm=s.model\nprint('actuators:')\nfor i,n in enumerate(rcb_dtb.ACTUATOR_NAMES):\n    a=m.actuator(n)\n    print(i,n,'gain',a.gainprm[0],'bias',a.biasprm[1],a.biasprm[2],'range',np.round(a.ctrlrange,3),'gear',a.gear[:3])\nprint('nq',m.nq,'nv',m.nv,'nbody',m.nbody)\n\"\n"}],"out":{"text":"0 dpos [ 0.03  -0.009  0.1  ] dang 16.05 spin_x 10.68 cum_spin 10.7\n1 dpos [-0.009 -0.003  0.042] dang 10.7 spin_x 10.36 cum_spin 21.0\n2 dpos [-0.022  0.004  0.032] dang 16.91 spin_x -14.7 cum_spin 6.3\n3 dpos [ 0.001 -0.003  0.019] dang 18.24 spin_x -18.08 cum_spin -11.7\n4 dpos [ 0.    -0.     0.014] dang 9.51 spin_x -9.47 cum_spin -21.2\n5 dpos [0.001 0.003 0.007] dang 0.78 spin_x 0.69 cum_spin -20.5\n6 dpos [ 0.002 -0.004  0.01 ] dang 3.23 spin_x -3.14 cum_spin -23.7\n7 dpos [-0.003  0.002  0.006] dang 3.98 spin_x 2.35 cum_spin -21.3\n8 dpos [ 0.002 -0.003  0.005] dang 15.99 spin_x 13.2 cum_spin -8.1\n9 dpos [-0.007 -0.006  0.002] dang 13.44 spin_x 11.2 cum_spin 3.1\n10 dpos [ 0.003  0.001 -0.001] dang 27.47 spin_x 24.01 cum_spin 27.1\n11 dpos [ 0.002  0.006 -0.002] dang 14.47 spin_x -12.78 cum_spin 14.3\n12 dpos [-0.003  0.007  0.005] dang 8.42 spin_x -4.21 cum_spin 10.1\n13 dpos [-0.003  0.004  0.001] dang 6.2 spin_x 1.03 cum_spin 11.2\n14 dpos [-0.003  0.002 -0.001] dang 9.32 spin_x 5.01 cum_spin 16.2\n15 dpos [-0.004  0.003  0.001] dang 12.63 spin_x 7.07 cum_spin 23.2\n16 dpos [-0.01  -0.004  0.011] dang 14.27 spin_x 11.5 cum_spin 34.7\n17 dpos [ 0.006  0.003 -0.   ] dang 7.58 spin_x -4.7 cum_spin 30.0\n18 dpos [ 0.005 -0.009 -0.002] dang 1.91 spin_x -0.89 cum_spin 29.1\n19 dpos [-0.    -0.017  0.004] dang 5.19 spin_x -0.22 cum_spin 28.9\n20 dpos [ 0.003 -0.022  0.005] dang 2.99 spin_x 2.98 cum_spin 31.9\n21 dpos [ 0.007 -0.021  0.003] dang 1.38 spin_x -0.61 cum_spin 31.3\n22 dpos [ 0.00\n… (9,303 characters left out) …\nlmleft_index_DIP_ctrl gain 0.9 bias -0.9 -0.03504460829068826 range [0.    1.396] gear [1. 0. 0.]\n16 palmleft_middle_MCP_FE_ctrl gain 4.76 bias -4.76 -0.20859229653879285 range [-0.175  1.571] gear [1. 0. 0.]\n17 palmleft_middle_MCP_AA_ctrl gain 6.62 bias -6.62 -0.24595531718141989 range [-0.349  0.349] gear [1. 0. 0.]\n18 palmleft_middle_PIP_ctrl gain 0.9 bias -0.9 -0.04243185147728897 range [0.    1.745] gear [1. 0. 0.]\n19 palmleft_middle_DIP_ctrl gain 0.9 bias -0.9 -0.035044608290688326 range [0.    1.396] gear [1. 0. 0.]\n20 palmleft_ring_MCP_FE_ctrl gain 4.76 bias -4.76 -0.2085922615226346 range [-0.175  1.571] gear [1. 0. 0.]\n21 palmleft_ring_MCP_AA_ctrl gain 6.62 bias -6.62 -0.24595527589661415 range [-0.349  0.349] gear [1. 0. 0.]\n22 palmleft_ring_PIP_ctrl gain 0.9 bias -0.9 -0.04243183442070378 range [0.    1.745] gear [1. 0. 0.]\n23 palmleft_ring_DIP_ctrl gain 0.9 bias -0.9 -0.03504460020894225 range [0.    1.396] gear [1. 0. 0.]\n24 palmleft_pinky_CMC_ctrl gain 1.38 bias -1.38 -0.02782345008798993 range [0.    0.262] gear [1. 0. 0.]\n25 palmleft_pinky_MCP_FE_ctrl gain 4.76 bias -4.76 -0.2085922894633835 range [-0.175  1.571] gear [1. 0. 0.]\n26 palmleft_pinky_MCP_AA_ctrl gain 6.62 bias -6.62 -0.24595527589661426 range [-0.349  0.349] gear [1. 0. 0.]\n27 palmleft_pinky_PIP_ctrl gain 0.9 bias -0.9 -0.04243183442070367 range [0.    1.745] gear [1. 0. 0.]\n28 palmleft_pinky_DIP_ctrl gain 0.9 bias -0.9 -0.03504460020894227 range [0.    1.396] gear [1. 0. 0.]\nnq 36 nv 35 nbody 36","exit":[],"pics":[]},"tok":{"in":37981,"cached":30916,"out":5448,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":7,"t":229.1,"say":"","calls":[{"kind":"robot","label":"s=rcb_dtb.Session('/app/instance.json')","code":"python -I -c \"\nimport numpy as np, sys, time\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb, mujoco\ns=rcb_dtb.Session('/app/instance.json')\nm,d=s.model,s.data\nt0=time.time()\nfor i in range(200): s.step(s.targets)\nprint('200 control steps in',round(time.time()-t0,2),'s ->',round((time.time()-t0)/200*1000,2),'ms/step')\nprint('obj pos',d.xpos[s._obj], 'palm', d.xpos[m.body('palmworld').id])\nfor b in range(m.nbody):\n    nm=mujoco.mj_id2name(m,mujoco.mjtObj.mjOBJ_BODY,b)\n    if nm and ('palm' in nm or nm in ('object','link7','attachment_site')):\n        print(b,nm,np.round(d.xpos[b],3),np.round(d.xquat[b],3))\n\" 2>&1 | tail -60"},{"kind":"shell","label":"s=rcb_dtb.Session('/app/instance.json')","code":"python -I -c \"\nimport numpy as np, sys, time, imageio.v2 as iio\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb\ns=rcb_dtb.Session('/app/instance.json')\nimgs=[s.render(c) for c in ('front','side','top','oblique')]\niio.imwrite('/app/work/init_grid.png', np.concatenate([np.concatenate(imgs[0:2],1),np.concatenate(imgs[2:4],1)],0))\nprint('ok')\n\"; ls -la /app/work"}],"out":{"text":"200 control steps in 0.1 s -> 0.51 ms/step\nobj pos [-0.08102725  0.06637775  0.54652404] palm [-1.26838720e-04  1.77243964e-01  7.38601194e-01]\n8 link7 [-0.     0.222  0.744] [ 0.662  0.738  0.097 -0.088]\n9 palmworld [-0.     0.177  0.739] [ 0.662  0.738  0.097 -0.088]\n10 palmleft_hand_C_MC [-0.     0.128  0.733] [ 0.472  0.526  0.527 -0.472]\n11 palmleft_thumb_CMC_VL [-0.026  0.108  0.721] [-0.    -0.     0.745 -0.667]\n12 palmleft_thumb_MC [-0.026  0.107  0.726] [ 0.382  0.02   0.051 -0.923]\n13 palmleft_thumb_MCP_VL [-0.076  0.067  0.711] [ 0.374 -0.079  0.288 -0.878]\n14 palmleft_thumb_PP [-0.076  0.067  0.711] [ 0.321  0.209 -0.417 -0.824]\n15 palmleft_thumb_DP [-0.104  0.039  0.708] [ 0.374 -0.079  0.288 -0.878]\n16 palmleft_index_MCP_VL [-0.03   0.033  0.722] [ 0.526 -0.472  0.527 -0.472]\n17 palmleft_index_PP [-0.03   0.033  0.722] [ 0.706  0.038  0.039 -0.706]\n18 palmleft_index_MP [-0.03  -0.014  0.717] [ 0.526 -0.472  0.527 -0.472]\n19 palmleft_index_DP [-0.03  -0.046  0.713] [ 0.526 -0.472  0.527 -0.472]\n20 palmleft_middle_MCP_VL [-0.01   0.029  0.722] [ 0.526 -0.472  0.527 -0.472]\n21 palmleft_middle_PP [-0.01   0.029  0.722] [ 0.706  0.038  0.039 -0.706]\n22 palmleft_middle_MP [-0.01  -0.017  0.717] [ 0.526 -0.472  0.527 -0.472]\n23 palmleft_middle_DP [-0.01  -0.049  0.714] [ 0.526 -0.472  0.527 -0.472]\n24 palmleft_ring_MCP_VL [0.01  0.036 0.722] [ 0.526 -0.472  0.527 -0.472]\n25 palmleft_ring_PP [0.01  0.036 0.722] [ 0.706  0.038  0.039 -0.706]\n26 palmleft_ring_MP [ 0.01  -\n… (3,353 characters left out) …\n.12/site-packages/mujoco/egl/__init__.py\", line 131, in free\n  File \"/usr/local/lib/python3.12/site-packages/OpenGL/error.py\", line 230, in glCheckError\nOpenGL.raw.EGL._errors.EGLError: <exception str() failed>\ntotal 196\ndrwxr-xr-x 2 root root     35 Oct 24 19:33 .\ndrwxr-xr-x 1 root root     26 Oct 24 19:31 ..\n-rw-r--r-- 1 root root 198962 Oct 24 19:33 init_grid.png\n\n[stdout]\nok\nException ignored in: <function Renderer.__del__ at 0x7ff352b19760>\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.12/site-packages/mujoco/renderer.py\", line 340, in __del__\n  File \"/usr/local/lib/python3.12/site-packages/mujoco/renderer.py\", line 326, in close\n  File \"/usr/local/lib/python3.12/site-packages/mujoco/egl/__init__.py\", line 131, in free\n  File \"/usr/local/lib/python3.12/site-packages/OpenGL/error.py\", line 230, in glCheckError\nOpenGL.raw.EGL._errors.EGLError: <exception str() failed>\nException ignored in: <function GLContext.__del__ at 0x7ff352b18f40>\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.12/site-packages/mujoco/egl/__init__.py\", line 138, in __del__\n  File \"/usr/local/lib/python3.12/site-packages/mujoco/egl/__init__.py\", line 131, in free\n  File \"/usr/local/lib/python3.12/site-packages/OpenGL/error.py\", line 230, in glCheckError\nOpenGL.raw.EGL._errors.EGLError: <exception str() failed>\ntotal 196\ndrwxr-xr-x 2 root root     35 Oct 24 19:33 .\ndrwxr-xr-x 1 root root     26 Oct 24 19:31 ..\n-rw-r--r-- 1 root root 198962 Oct 24 19:33 init_grid.png","exit":[],"pics":[]},"tok":{"in":47385,"cached":37975,"out":1703,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":8,"t":256.7,"say":"","calls":[{"kind":"look","label":"init_grid.png","code":"/app/work/init_grid.png"}],"out":{"text":"[picture]\n\n[metadata] [picture]}","exit":[],"pics":["dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5/s008_0.jpg"]},"tok":{"in":51046,"cached":47379,"out":839,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":9,"t":522.7,"say":"","calls":[{"kind":"robot","label":"s=rcb_dtb.Session('/app/instance.json'); m,d=s.model,s.data","code":"python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb, mujoco\ns=rcb_dtb.Session('/app/instance.json'); m,d=s.model,s.data\n# settle\nfor i in range(120): s.step(s.targets)\nst=s.state(); print('settled obj',np.round(st['object_pos'],4),np.round(st['object_quat_wxyz'],4))\n# close fingers\ntg=s.targets.copy()\nnames=rcb_dtb.JOINT_NAMES\nvals={'thumb_CMC_FE':0.3,'thumb_MCP_FE':0.6,'thumb_IP':0.3,'index_MCP_FE':1.0,'index_PIP':1.2,'index_DIP':0.6,'middle_MCP_FE':1.0,'middle_PIP':1.2,'middle_DIP':0.6,'ring_MCP_FE':1.0,'ring_PIP':1.2,'ring_DIP':0.6,'pinky_MCP_FE':1.0,'pinky_PIP':1.2,'pinky_DIP':0.6}\nfor i,n in enumerate(names):\n    for k,v in vals.items():\n        if n=='palmleft_'+k: tg[i]=v\nfor i in range(60): s.step(tg)\npw=m.body('palmworld').id\nR=d.xmat[pw].reshape(3,3); p=d.xpos[pw]\ndef loc(b): return R.T@(d.xpos[m.body(b).id]-p)\nfor b in ['palmleft_thumb_DP','palmleft_index_DP','palmleft_middle_DP','palmleft_ring_DP','palmleft_pinky_DP','palmleft_index_PP','palmleft_middle_PP','palmleft_ring_PP','palmleft_pinky_PP','palmleft_thumb_PP']:\n    print(b, np.round(loc(b),3))\nprint('palm quat',np.round(d.xquat[pw],3))\n\""}],"out":{"text":"settled obj [-0.081   0.0626  0.5465] [ 0.5605  0.8274 -0.0068 -0.0344]\npalmleft_thumb_DP [-0.064 -0.062  0.149]\npalmleft_index_DP [-0.012 -0.072  0.153]\npalmleft_middle_DP [ 0.007 -0.065  0.156]\npalmleft_ring_DP [ 0.027 -0.062  0.15 ]\npalmleft_pinky_DP [ 0.048 -0.058  0.144]\npalmleft_index_PP [-0.029 -0.009  0.146]\npalmleft_middle_PP [-0.01  -0.003  0.149]\npalmleft_ring_PP [0.01  0.001 0.143]\npalmleft_pinky_PP [0.031 0.005 0.137]\npalmleft_thumb_PP [-0.062 -0.048  0.113]\npalm quat [ 0.662  0.738  0.097 -0.088]\n\n[stdout]\nsettled obj [-0.081   0.0626  0.5465] [ 0.5605  0.8274 -0.0068 -0.0344]\npalmleft_thumb_DP [-0.064 -0.062  0.149]\npalmleft_index_DP [-0.012 -0.072  0.153]\npalmleft_middle_DP [ 0.007 -0.065  0.156]\npalmleft_ring_DP [ 0.027 -0.062  0.15 ]\npalmleft_pinky_DP [ 0.048 -0.058  0.144]\npalmleft_index_PP [-0.029 -0.009  0.146]\npalmleft_middle_PP [-0.01  -0.003  0.149]\npalmleft_ring_PP [0.01  0.001 0.143]\npalmleft_pinky_PP [0.031 0.005 0.137]\npalmleft_thumb_PP [-0.062 -0.048  0.113]\npalm quat [ 0.662  0.738  0.097 -0.088]","exit":[],"pics":[]},"tok":{"in":52160,"cached":12864,"out":11753,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":10,"t":646.4,"say":"The situation is clear: the tool needs to be lifted, tilted vertically, and then rotated about its own (vertical) axis by about 830° — more than the wrist joint can handle on its own. So the fingers have to roll it (finger-gaiting ratchet), and the arm has to follow position and tilt. Writing the generator now.","calls":[{"kind":"edit","label":"gen.py","code":"/app/work/gen.py\n\nimport numpy as np, sys, time, json\nsys.path.insert(0, '/opt/tools')\nimport rcb_dtb, mujoco\n\nOUT = '/app/output/trajectory.npz'\ns = rcb_dtb.Session('/app/instance.json')\nm, d = s.model, s.data\nN = 29\nIID = list(range(7))           # iiwa dof indices in qpos/ctrl\nDOF = np.array([m.joint(f'joint{i+1}').dofadr for i in range(7)])\nPALM = m.body('palmworld').id\nOBJ = m.body('object').id\nLOWER, UPPER = s.lower.copy(), s.upper.copy()\n\ndef quat_mul(a, b):\n    w1,x1,y1,z1 = a; w2,x2,y2,z2 = b\n    return np.array([w1*w2-x1*x2-y1*y2-z1*z2, w1*x2+x1*w2+y1*z2-z1*y2,\n                     w1*y2-x1*z2+y1*w2+z1*x2, w1*z2+x1*y2-y1*x2+z1*w2])\ndef quat_inv(a):\n    w,x,y,z = a; return np.array([w,-x,-y,-z])\ndef quat_mat(q):\n    w,x,y,z = q\n    return np.array([[1-2*(y*y+z*z),2*(x*y-z*w),2*(x*z+y*w)],\n                     [2*(x*y+z*w),1-2*(x*x+z*z),2*(y*z-x*w)],\n                     [2*(x*z-y*w),2*(y*z+x*w),1-2*(x*x+y*y)]])\ndef mat_quat(R):\n    t = R[0,0]+R[1,1]+R[2,2]\n    if t > 0:\n        s2 = np.sqrt(t+1)*2; w=s2/4; x=(R[2,1]-R[1,2])/s2; y=(R[0,2]-R[2,0])/s2; z=(R[1,0]-R[0,1])/s2\n    elif R[0,0] > R[1,1] and R[0,0] > R[2,2]:\n        s2=np.sqrt(1+R[0,0]-R[1,1]-R[2,2])*2; w=(R[2,1]-R[1,2])/s2; x=s2/4; y=(R[0,1]+R[1,0])/s2; z=(R[0,2]+R[2,0])/s2\n    elif R[1,1] > R[2,2]:\n        s2=np.sqrt(1+R[1,1]-R[0,0]-R[2,2])*2; w=(R[0,2]-R[2,0])/s2; x=(R[0,1]+R[1,0])/s2; y=s2/4; z=(R[1,2]+R[2,1])/s2\n    else:\n        s2=np.sqrt(1+R[2,2]-R[0,0]-R[1,1])*2; w=(R[1,0]-R[0,1])/s2; x=(R[0,2]+R[2,0])/s2; y=(R[1,2]+R[2,1])/s2; z=s2/4\n    q=np.array([w,x,y,z]); return q/np.linalg.norm(q)\ndef slerp(a, b, t):\n    dot = np.clip(np.dot(a,b), -1, 1)\n    if dot > 0.9995:\n        q = a + t*(b-a); return q/np.linalg.norm(q)\n    th = np.arccos(dot); return (np.sin((1-t)*th)*a + np.sin(t*th)*b)/np.sin(th)\ndef rot_err_vec(Rc, Rt):\n    # error w in world s.t. Rt ~= R(w) Rc\n    Re = Rt @ Rc.T\n    return 0.5*np.array([Re[2,1]-Re[1,2], Re[0,2]-Re[2,0], Re[1,0]-Re[0,1]]) / max(1e-9, min(1.0, abs(0.5*np.sqrt(max(0,(Re[0,0]+Re[1,1]+Re[2,2]-1)**2)) ) or 1.0)) * 1.0\n\ndef rot_err(Rc, Rt):\n    Re = Rt @ Rc.T\n    v = 0.5*np.array([Re[2,1]-Re[1,2], Re[0,2]-Re[2,0], Re[1,0]-Re[0,1]])\n    c = (np.trace(Re)-1)/2\n    ang = np.arccos(np.clip(c,-1,1))\n    n = np.linalg.norm(v)\n    if n < 1e-9: return np.zeros(3)\n    return v/n*ang\n\n# ---------- finger postures ----------\nFIDX = {n: i for i, n in enumerate(rcb_dtb.JOINT_NAMES)}\ndef base_targets():\n    t = rcb_dtb.INIT_Q.copy()\n    return t\nGRASP = {}\ndef set_f(t, name, v): t[FIDX['palmleft_'+name]] = v\ndef fingers_open(t):\n    for f in ['index','middle','ring','pinky']:\n        set_f(t, f+'_MCP_FE', -0.1); set_f(t, f+'_PIP', 0.0); set_f(t, f+'_DIP', 0.0); set_f(t, f+'_MCP_AA', 0.0)\n    set_f(t,'thumb_CMC_FE', -0.1); set_f(t,'thumb_MCP_FE', -0.3); set_f(t,'thumb_IP', 0.0)\n    return t\n\n# ---------- IK ----------\ndef ik_step(tgt_pos, tgt_R, gain=1.0, maxdq=0.06, cur=None):\n    q = d.qpos[DOF].copy()\n    Jp = np.zeros((3, m.nv)); Ja = np.zeros((3, m.nv))\n\n… (1,115 characters left out) …\nor i in range(d.ncon):\n        c = d.contact[i]\n        g1, g2 = c.geom1, c.geom2\n        for g in (g1, g2):\n            nm = mujoco.mj_id2name(m, mujoco.mjtObj.mjOBJ_GEOM, g) or ''\n            if nm.startswith('palmleft'): ns.add(nm.split('_object')[0])\n    return ns\n\nrec = []\ndef S(t):\n    p = s.step(t); rec.append(t.copy()); return p\n\n# ============ stage 0: settle ============\nt0 = base_targets()\nfor i in range(90): S(t0)\nobj0 = d.xpos[OBJ].copy(); R0 = d.xmat[OBJ].reshape(3,3).copy()\nprint('settled', np.round(obj0,4))\n\n# ============ grasp geometry ============\n# tool axis (x) direction on table:\nax = R0[:,0]\nax[2] = 0; ax /= np.linalg.norm(ax)          # horizontal tool axis\n# palm frame: x = tool axis, y = up, z = x cross y? ensure right-handed\npy = np.array([0,0,1.0])\npx = ax\npz = np.cross(px, py)\nRp = np.stack([px, py, pz], axis=1)\ntp_off = np.array([0.0, -0.035, 0.16])        # tool origin in palm frame\npalm_pos0 = obj0 - Rp @ tp_off\n\nt = base_targets(); fingers_open(t)\n# approach: interpolate palm pose from current to pregrasp (a bit higher)\nRc = d.xmat[PALM].reshape(3,3); pc = d.xpos[PALM].copy()\nqc = d.xquat[PALM].copy(); qp = mat_quat(Rp)\nif np.dot(qc, qp) < 0: qp = -qp\nNA = 160\nfor i in range(NA):\n    u = (i+1)/NA\n    u = u*u*(3-2*u)\n    pos = pc + u*((palm_pos0 + np.array([0,0,0.08])) - pc)\n    R = slerp(qc, qp, u)\n    t[:7] = ik_step(pos, quat_mat(R), gain=1.0, maxdq=0.08)\n    S(t)\n# descend\nND = 70\nfor i in range(ND):\n    u = (i+1)/ND\n    pos = palm_pos0 + np.array([0,0,0.08])*(1-u)\n    t[:7] = ik_step(pos, Rp, gain=1.0, maxdq=0.05)\n    S(t)\nprint('pregrasp palm err', np.round(d.xpos[PALM]-palm_pos0,3))\n# close fingers until contact\ntg = t.copy()\nclosed = False\nfor i in range(140):\n    u = (i+1)/140.0\n    for f in ['index','middle','ring','pinky']:\n        set_f(tg, f+'_MCP_FE', -0.1 + u*1.35)\n        set_f(tg, f+'_PIP', u*1.3)\n        set_f(tg, f+'_DIP', u*0.7)\n    set_f(tg,'thumb_CMC_FE', -0.1+u*0.5); set_f(tg,'thumb_MCP_FE', -0.3+u*0.9); set_f(tg,'thumb_IP', u*0.4)\n    tg[:7] = ik_step(palm_pos0, Rp, gain=1.0, maxdq=0.03)\n    p = S(tg)\n    cn = contacts_with_object()\n    if len(cn) >= 3 and i > 60: closed = True; break\nprint('closed', closed, 'contacts', contacts_with_object(), 'step', len(rec))\ngrasp_t = tg.copy()\n# lift test\nfor i in range(80):\n    u = (i+1)/80\n    pos = palm_pos0 + np.array([0,0,0.10])*u\n    tg[:7] = ik_step(pos, Rp, gain=1.0, maxdq=0.05)\n    S(tg)\nliftok = abs(d.xpos[OBJ][2] - (obj0[2]+0.10)) < 0.03\nprint('lift test', liftok, 'obj', np.round(d.xpos[OBJ],3), 'dist', s.goal_distance())\n# measure tool-in-palm\nRpalm = d.xmat[PALM].reshape(3,3); ppalm = d.xpos[PALM]\nT_tp_pos = Rpalm.T @ (d.xpos[OBJ]-ppalm)\nT_tp_R = Rpalm.T @ d.xmat[OBJ].reshape(3,3)\nprint('tool in palm', np.round(T_tp_pos,3), np.round(T_tp_R,2))\nnp.save('/app/work/T_tp.npy', np.concatenate([T_tp_pos, mat_quat(T_tp_R)]))\nnp.save('/app/work/grasp_t.npy', grasp_t)\nnp.save('/app/work/rec0.npy', np.array(rec))\nprint('stage1 done, steps', len(rec))\n"}],"out":{"text":"File created successfully at: /app/work/gen.py (file state is current in your context — no need to Read it back)","exit":[],"pics":[]},"tok":{"in":64280,"cached":52154,"out":8549,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":11,"t":712.8,"say":"","calls":[{"kind":"shell","label":"Run grasp stage 1","code":"timeout 300 python -I gen.py 2>&1 | tail -20"}],"out":{"text":"settled [-0.0802  0.0751  0.5484]\nTraceback (most recent call last):\n  File \"/app/work/gen.py\", line 144, in <module>\n    t[:7] = ik_step(pos, quat_mat(R), gain=1.0, maxdq=0.08)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/work/gen.py\", line 77, in ik_step\n    A = J @ J.T + 1e-3*np.eye(6)\n        ~~^~~~~\nValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 7 is different from 1)\n\n[stdout]\nsettled [-0.0802  0.0751  0.5484]\nTraceback (most recent call last):\n  File \"/app/work/gen.py\", line 144, in <module>\n    t[:7] = ik_step(pos, quat_mat(R), gain=1.0, maxdq=0.08)\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/work/gen.py\", line 77, in ik_step\n    A = J @ J.T + 1e-3*np.eye(6)\n        ~~^~~~~\nValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 7 is different from 1)","exit":[],"pics":[]},"tok":{"in":72870,"cached":64274,"out":131,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":12,"t":732.4,"say":"","calls":[{"kind":"shell","label":"s=rcb_dtb.Session('/app/instance.json'); m,d=s.model,s.data","code":"python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb, mujoco\ns=rcb_dtb.Session('/app/instance.json'); m,d=s.model,s.data\nDOF=np.array([m.joint(f'joint{i+1}').dofadr for i in range(7)]); print(DOF)\nJp=np.zeros((3,m.nv)); Ja=np.zeros((3,m.nv))\nmujoco.mj_jacBody(m,d,Jp,Ja,m.body('palmworld').id)\nprint(Jp.shape, Jp[:,DOF].shape, np.vstack([Jp[:,DOF],Ja[:,DOF]]).shape)\n\""}],"out":{"text":"[[0]\n [1]\n [2]\n [3]\n [4]\n [5]\n [6]]\n(3, 35) (3, 7, 1) (6, 7, 1)\n\n[stdout]\n[[0]\n [1]\n [2]\n [3]\n [4]\n [5]\n [6]]\n(3, 35) (3, 7, 1) (6, 7, 1)","exit":[],"pics":[]},"tok":{"in":73204,"cached":72864,"out":901,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":13,"t":741.7,"say":"","calls":[{"kind":"shell","label":"Fix dof adr and rerun stage 1","code":"python - <<'EOF'\nimport re\np='/app/work/gen.py'\nsrc=open(p).read()\nsrc=src.replace(\"DOF = np.array([m.joint(f'joint{i+1}').dofadr for i in range(7)])\",\n                \"DOF = np.array([int(m.joint(f'joint{i+1}').dofadr[0]) for i in range(7)])\")\nopen(p,'w').write(src)\nEOF\ntimeout 300 python -I gen.py 2>&1 | tail -12"}],"out":{"text":"settled [-0.0802  0.0751  0.5484]\npregrasp palm err [-0.    -0.     0.006]\nclosed True contacts {'palmleft_pinky_elastomer', 'palmleft_index_elastomer', 'palmleft_ring_elastomer', 'palmleft_index_PP', 'palmleft_thumb_elastomer', 'palmleft_middle_elastomer'} step 382\nlift test False obj [-0.081  0.065  0.546] dist 0.18742615235052157\ntool in palm [-0.   -0.13  0.17] [[ 1.    0.03 -0.07]\n [-0.04  0.99 -0.13]\n [ 0.07  0.13  0.99]]\nstage1 done, steps 462\n\n[stdout]\nsettled [-0.0802  0.0751  0.5484]\npregrasp palm err [-0.    -0.     0.006]\nclosed True contacts {'palmleft_pinky_elastomer', 'palmleft_index_elastomer', 'palmleft_ring_elastomer', 'palmleft_index_PP', 'palmleft_thumb_elastomer', 'palmleft_middle_elastomer'} step 382\nlift test False obj [-0.081  0.065  0.546] dist 0.18742615235052157\ntool in palm [-0.   -0.13  0.17] [[ 1.    0.03 -0.07]\n [-0.04  0.99 -0.13]\n [ 0.07  0.13  0.99]]\nstage1 done, steps 462","exit":[],"pics":[]},"tok":{"in":74173,"cached":73198,"out":225,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":14,"t":793.4,"say":"","calls":[{"kind":"edit","label":"grasp_sweep.py","code":"/app/work/grasp_sweep.py\n\nimport numpy as np, sys, itertools, json\nsys.path.insert(0, '/opt/tools')\nimport rcb_dtb, mujoco\n\ns = rcb_dtb.Session('/app/instance.json')\nm, d = s.model, s.data\nDOF = np.array([int(m.joint(f'joint{i+1}').dofadr[0]) for i in range(7)])\nPALM = m.body('palmworld').id; OBJ = m.body('object').id\nLOWER, UPPER = s.lower.copy(), s.upper.copy()\nFIDX = {n: i for i, n in enumerate(rcb_dtb.JOINT_NAMES)}\n\ndef quat_mat(q):\n    w,x,y,z = q\n    return np.array([[1-2*(y*y+z*z),2*(x*y-z*w),2*(x*z+y*w)],\n                     [2*(x*y+z*w),1-2*(x*x+z*z),2*(y*z-x*w)],\n                     [2*(x*z-y*w),2*(y*z+x*w),1-2*(x*x+y*y)]])\ndef mat_quat(R):\n    t = R[0,0]+R[1,1]+R[2,2]\n    if t > 0:\n        s2 = np.sqrt(t+1)*2; w=s2/4; x=(R[2,1]-R[1,2])/s2; y=(R[0,2]-R[2,0])/s2; z=(R[1,0]-R[0,1])/s2\n    elif R[0,0] > R[1,1] and R[0,0] > R[2,2]:\n        s2=np.sqrt(1+R[0,0]-R[1,1]-R[2,2])*2; w=(R[2,1]-R[1,2])/s2; x=s2/4; y=(R[0,1]+R[1,0])/s2; z=(R[0,2]+R[2,0])/s2\n    elif R[1,1] > R[2,2]:\n        s2=np.sqrt(1+R[1,1]-R[0,0]-R[2,2])*2; w=(R[0,2]-R[2,0])/s2; x=(R[0,1]+R[1,0])/s2; y=s2/4; z=(R[1,2]+R[2,1])/s2\n    else:\n        s2=np.sqrt(1+R[2,2]-R[0,0]-R[1,1])*2; w=(R[1,0]-R[0,1])/s2; x=(R[0,2]+R[2,0])/s2; y=(R[1,2]+R[2,1])/s2; z=s2/4\n    q=np.array([w,x,y,z]); return q/np.linalg.norm(q)\ndef slerp(a,b,t):\n    dot=np.clip(np.dot(a,b),-1,1)\n    if dot>0.9995: q=a+t*(b-a); return q/np.linalg.norm(q)\n    th=np.arccos(dot); return (np.sin((1-t)*th)*a+np.sin(t*th)*b)/np.sin(th)\ndef rot_err(Rc,Rt):\n    Re = Rt@Rc.T\n    v = 0.5*np.array([Re[2,1]-Re[1,2],Re[0,2]-Re[2,0],Re[1,0]-Re[0,1]])\n    ang = np.arccos(np.clip((np.trace(Re)-1)/2,-1,1)); n=np.linalg.norm(v)\n    return np.zeros(3) if n<1e-9 else v/n*ang\ndef ik_step(tgt_pos, tgt_R, gain=1.0, maxdq=0.06):\n    q = d.qpos[DOF].copy()\n    Jp = np.zeros((3,m.nv)); Ja = np.zeros((3,m.nv))\n    mujoco.mj_jacBody(m,d,Jp,Ja,PALM)\n    J = np.vstack([Jp[:,DOF],Ja[:,DOF]])\n    e = np.concatenate([tgt_pos-d.xpos[PALM], rot_err(d.xmat[PALM].reshape(3,3), tgt_R)])\n    dq = J.T @ np.linalg.solve(J@J.T+1e-3*np.eye(6), e)*gain\n    dq = np.clip(dq,-maxdq,maxdq)\n    return np.clip(q+dq, LOWER[:7], UPPER[:7])\ndef set_f(t,name,v): t[FIDX['palmleft_'+name]] = v\n\ndef trial(off_y, off_z, close, thumb=(0.4,0.9,0.4), verbose=False):\n    s.reset(); rec=[]\n    t = rcb_dtb.INIT_Q.copy()\n    for i in range(90):\n        p=s.step(t); rec.append(t.copy())\n    obj0 = d.xpos[OBJ].copy(); R0 = d.xmat[OBJ].reshape(3,3).copy()\n    ax = R0[:,0].copy(); ax[2]=0; ax/=np.linalg.norm(ax)\n    Rp = np.stack([ax, np.array([0,0,1.0]), np.cross(ax,[0,0,1.0])], axis=1)\n    palm0 = obj0 - Rp@np.array([0.0, off_y, off_z])\n    for f in ['index','middle','ring','pinky']:\n        set_f(t,f+'_MCP_FE',-0.1); set_f(t,f+'_PIP',0.0); set_f(t,f+'_DIP',0.0)\n    set_f(t,'thumb_CMC_FE',-0.1); set_f(t,'thumb_MCP_FE',-0.3); set_f(t,'thumb_IP',0.0)\n    qc = d.xquat[PALM].copy(); pc = d.xpos[PALM].copy(); qp = mat_quat(Rp)\n    if np.dot(qc,qp)<0: qp=-qp\n    for i in range(150):\n        u=(i+1)/150; u=u*u*(3-2*u)\n        t[:7]=ik_step(pc+u*((palm0+np.array([0,0,0.08]))-pc), quat_mat(slerp(qc,qp,u)), 1.0, 0.08)\n        p=s.step(t); rec.append(t.copy())\n    for i in range(70):\n        u=(i+1)/70\n        t[:7]=ik_step(palm0+np.array([0,0,0.08])*(1-u), Rp, 1.0, 0.05)\n        p=s.step(t); rec.append(t.copy())\n    tg=t.copy()\n    for i in range(140):\n        u=(i+1)/140.0\n        for f in ['index','middle','ring','pinky']:\n            set_f(tg,f+'_MCP_FE',-0.1+u*(close+0.1)); set_f(tg,f+'_PIP',u*close*1.1); set_f(tg,f+'_DIP',u*close*0.6)\n        set_f(tg,'thumb_CMC_FE',-0.1+u*(thumb[0]+0.1)); set_f(tg,'thumb_MCP_FE',-0.3+u*(thumb[1]+0.3)); set_f(tg,'thumb_IP',u*thumb[2])\n        tg[:7]=ik_step(palm0, Rp, 1.0, 0.03)\n        p=s.step(tg); rec.append(tg.copy())\n    gq = tg.copy()\n    for i in range(80):\n        u=(i+1)/80\n        tg[:7]=ik_step(palm0+np.array([0,0,0.12])*u, Rp, 1.0, 0.05)\n        p=s.step(tg); rec.append(tg.copy())\n    exp = obj0+np.array([0,0,0.12])\n    slip = np.linalg.norm(d.xpos[OBJ]-exp)\n    # hold test\n    for i in range(40):\n        tg[:7]=ik_step(palm0+np.array([0,0,0.12]), Rp, 1.0, 0.03)\n        p=s.step(tg); rec.append(tg.copy())\n    slip2 = np.linalg.norm(d.xpos[OBJ]-(obj0+np.array([0,0,0.12])))\n    if verbose: print('   obj', np.round(d.xpos[OBJ],3), 'ncon', d.ncon)\n    return slip, slip2, rec, gq\n\nif __name__ == '__main__':\n    res=[]\n    for off_y, off_z, close in itertools.product([-0.03,-0.05,-0.07],[0.14,0.16],[1.2,1.5]):\n        sl, sl2, rec, gq = trial(off_y, off_z, close)\n        res.append((sl+sl2, off_y, off_z, close))\n        print(f'off_y={off_y} off_z={off_z} close={close} slip={sl:.3f} slip2={sl2:.3f}')\n    res.sort()\n    print('BEST', res[0])\n"}],"out":{"text":"File created successfully at: /app/work/grasp_sweep.py (file state is current in your context — no need to Read it back)","exit":[],"pics":[]},"tok":{"in":74731,"cached":74167,"out":4119,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":15,"t":831.2,"say":"","calls":[{"kind":"shell","label":"Sweep grasp parameters","code":"timeout 500 python -I grasp_sweep.py 2>&1 | tail -15"}],"out":{"text":"off_y=-0.03 off_z=0.14 close=1.2 slip=0.706 slip2=0.692\noff_y=-0.03 off_z=0.14 close=1.5 slip=0.166 slip2=0.164\noff_y=-0.03 off_z=0.16 close=1.2 slip=0.693 slip2=0.693\noff_y=-0.03 off_z=0.16 close=1.5 slip=0.175 slip2=0.167\noff_y=-0.05 off_z=0.14 close=1.2 slip=0.709 slip2=0.729\noff_y=-0.05 off_z=0.14 close=1.5 slip=0.139 slip2=0.137\noff_y=-0.05 off_z=0.16 close=1.2 slip=0.180 slip2=0.178\noff_y=-0.05 off_z=0.16 close=1.5 slip=0.735 slip2=0.779\noff_y=-0.07 off_z=0.14 close=1.2 slip=0.761 slip2=0.761\noff_y=-0.07 off_z=0.14 close=1.5 slip=0.673 slip2=0.674\noff_y=-0.07 off_z=0.16 close=1.2 slip=0.747 slip2=0.739\noff_y=-0.07 off_z=0.16 close=1.5 slip=0.692 slip2=0.692\nBEST (0.2753270163970377, -0.05, 0.14, 1.5)\n\n[stdout]\noff_y=-0.03 off_z=0.14 close=1.2 slip=0.706 slip2=0.692\noff_y=-0.03 off_z=0.14 close=1.5 slip=0.166 slip2=0.164\noff_y=-0.03 off_z=0.16 close=1.2 slip=0.693 slip2=0.693\noff_y=-0.03 off_z=0.16 close=1.5 slip=0.175 slip2=0.167\noff_y=-0.05 off_z=0.14 close=1.2 slip=0.709 slip2=0.729\noff_y=-0.05 off_z=0.14 close=1.5 slip=0.139 slip2=0.137\noff_y=-0.05 off_z=0.16 close=1.2 slip=0.180 slip2=0.178\noff_y=-0.05 off_z=0.16 close=1.5 slip=0.735 slip2=0.779\noff_y=-0.07 off_z=0.14 close=1.2 slip=0.761 slip2=0.761\noff_y=-0.07 off_z=0.14 close=1.5 slip=0.673 slip2=0.674\noff_y=-0.07 off_z=0.16 close=1.2 slip=0.747 slip2=0.739\noff_y=-0.07 off_z=0.16 close=1.5 slip=0.692 slip2=0.692\nBEST (0.2753270163970377, -0.05, 0.14, 1.5)","exit":[],"pics":[]},"tok":{"in":78894,"cached":74725,"out":77,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":16,"t":852.0,"say":"","calls":[{"kind":"shell","label":"Debug grasp contact details","code":"timeout 300 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\nimport imageio.v2 as iio\ns=G.s; m=G.d and G.m; d=G.d\nsl,sl2,rec,gq = G.trial(-0.05,0.14,1.5,verbose=True)\nprint('slip',sl,sl2)\niio.imwrite('/app/work/grasp_end.png', np.concatenate([np.concatenate([s.render('front'),s.render('side')],1),np.concatenate([s.render('top'),s.render('oblique')],1)],0))\n# contact detail\nfor i in range(d.ncon):\n    c=d.contact[i]\n    n1=G.mujoco.mj_id2name(m,G.mujoco.mjtObj.mjOBJ_GEOM,c.geom1); n2=G.mujoco.mj_id2name(m,G.mujoco.mjtObj.mjOBJ_GEOM,c.geom2)\n    print(i,n1,n2,'dist',round(c.dist,4))\n\" 2>&1 | tail -20"}],"out":{"text":"obj [-0.093  0.158  0.56 ] ncon 3\nslip 0.13855448499116402 0.13677253140587373\n0 None object_object_geom_decomp_0 dist -0.0001\n1 table_geom object_object_geom_decomp_1 dist -0.0002\n2 table_geom object_object_geom_decomp_1 dist -0.0002\n\n[stdout]\n   obj [-0.093  0.158  0.56 ] ncon 3\nslip 0.13855448499116402 0.13677253140587373\n0 None object_object_geom_decomp_0 dist -0.0001\n1 table_geom object_object_geom_decomp_1 dist -0.0002\n2 table_geom object_object_geom_decomp_1 dist -0.0002","exit":[],"pics":[]},"tok":{"in":79444,"cached":78888,"out":551,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":17,"t":912.6,"say":"","calls":[{"kind":"robot","label":"Capture closing renders","code":"timeout 300 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\nimport imageio.v2 as iio\ns=G.s; m=G.m; d=G.d\n# replicate trial up to end of closing, with renders\nimport importlib\nsrc=open('/app/work/grasp_sweep.py').read()\n# manual run\noff_y,off_z,close=-0.05,0.14,1.5\ns.reset()\nt=G.rcb_dtb.INIT_Q.copy()\nfor i in range(90): s.step(t)\nobj0=d.xpos[G.OBJ].copy(); R0=d.xmat[G.OBJ].reshape(3,3).copy()\nax=R0[:,0].copy(); ax[2]=0; ax/=np.linalg.norm(ax)\nRp=np.stack([ax,np.array([0,0,1.0]),np.cross(ax,[0,0,1.0])],axis=1)\npalm0=obj0-Rp@np.array([0.0,off_y,off_z])\nfor f in ['index','middle','ring','pinky']:\n    G.set_f(t,f+'_MCP_FE',-0.1); G.set_f(t,f+'_PIP',0.0); G.set_f(t,f+'_DIP',0.0)\nG.set_f(t,'thumb_CMC_FE',-0.1); G.set_f(t,'thumb_MCP_FE',-0.3); G.set_f(t,'thumb_IP',0.0)\nqc=d.xquat[G.PALM].copy(); pc=d.xpos[G.PALM].copy(); qp=G.mat_quat(Rp)\nif np.dot(qc,qp)<0: qp=-qp\nfor i in range(150):\n    u=(i+1)/150; u=u*u*(3-2*u)\n    t[:7]=G.ik_step(pc+u*((palm0+np.array([0,0,0.08]))-pc), G.quat_mat(G.slerp(qc,qp,u)),1.0,0.08); s.step(t)\nfor i in range(70):\n    u=(i+1)/70\n    t[:7]=G.ik_step(palm0+np.array([0,0,0.08])*(1-u), Rp,1.0,0.05); s.step(t)\niio.imwrite('/app/work/pre_close.png', np.concatenate([s.render('front'),s.render('oblique')],1))\ntg=t.copy()\nfor i in range(140):\n    u=(i+1)/140.0\n    for f in ['index','middle','ring','pinky']:\n        G.set_f(tg,f+'_MCP_FE',-0.1+u*(close+0.1)); G.set_f(tg,f+'_PIP',u*close*1.1); G.set_f(tg,f+'_DIP',u*close*0.6)\n    G.set_f(tg,'thumb_CMC_FE',-0.1+u*0.5); G.set_f(tg,'thumb_MCP_FE',-0.3+u*1.2); G.set_f(tg,'thumb_IP',u*0.4)\n    tg[:7]=G.ik_step(palm0,Rp,1.0,0.03); s.step(tg)\n    if i in (60,100,139):\n        iio.imwrite(f'/app/work/close_{i}.png', np.concatenate([s.render('front'),s.render('oblique')],1))\n        print(i,'obj',np.round(d.xpos[G.OBJ],3),'palm',np.round(d.xpos[G.PALM],3))\nprint('tool in palm', np.round(d.xmat[G.PALM].reshape(3,3).T@(d.xpos[G.OBJ]-d.xpos[G.PALM]),3))\n\" 2>&1 | tail -6"}],"out":{"text":"60 obj [-0.081  0.068  0.547] palm [-0.069  0.216  0.6  ]\n100 obj [-0.08   0.066  0.547] palm [-0.071  0.218  0.607]\n139 obj [-0.071  0.156  0.549] palm [-0.072  0.222  0.612]\ntool in palm [ 0.006 -0.066  0.063]\n\n[stdout]\n60 obj [-0.081  0.068  0.547] palm [-0.069  0.216  0.6  ]\n100 obj [-0.08   0.066  0.547] palm [-0.071  0.218  0.607]\n139 obj [-0.071  0.156  0.549] palm [-0.072  0.222  0.612]\ntool in palm [ 0.006 -0.066  0.063]","exit":[],"pics":[]},"tok":{"in":80134,"cached":79438,"out":3158,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":18,"t":935.6,"say":"","calls":[{"kind":"look","label":"close_139.png","code":"/app/work/close_139.png"}],"out":{"text":"[picture]\n\n[metadata] [picture]}","exit":[],"pics":["dextoolbench_screwdriver_long_screwdriver_spin_vertical-privileged--jvayqr5/s018_0.jpg"]},"tok":{"in":83472,"cached":80128,"out":187,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":19,"t":1004.3,"say":"","calls":[{"kind":"robot","label":"Measure fully closed fingertip pocket","code":"timeout 120 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/opt/tools')\nimport rcb_dtb, mujoco\ns=rcb_dtb.Session('/app/instance.json'); m,d=s.model,s.data\nFIDX={n:i for i,n in enumerate(rcb_dtb.JOINT_NAMES)}\nt=rcb_dtb.INIT_Q.copy()\nfor f in ['index','middle','ring','pinky']:\n    t[FIDX['palmleft_'+f+'_MCP_FE']]=1.571; t[FIDX['palmleft_'+f+'_PIP']]=1.745; t[FIDX['palmleft_'+f+'_DIP']]=1.396\nt[FIDX['palmleft_thumb_CMC_FE']]=0.6; t[FIDX['palmleft_thumb_MCP_FE']]=1.0; t[FIDX['palmleft_thumb_IP']]=1.0\nfor i in range(120): s.step(t)\nR=d.xmat[m.body('palmworld').id].reshape(3,3); p=d.xpos[m.body('palmworld').id]\nfor b in ['palmleft_index_DP','palmleft_middle_DP','palmleft_ring_DP','palmleft_pinky_DP','palmleft_thumb_DP','palmleft_index_MP','palmleft_middle_MP']:\n    print(b, np.round(R.T@(d.xpos[m.body(b).id]-p),3))\n\""}],"out":{"text":"palmleft_index_DP [-0.016 -0.052  0.114]\npalmleft_middle_DP [ 0.002 -0.048  0.119]\npalmleft_ring_DP [ 0.022 -0.043  0.112]\npalmleft_pinky_DP [ 0.042 -0.038  0.106]\npalmleft_thumb_DP [-0.039 -0.067  0.147]\npalmleft_index_MP [-0.016 -0.054  0.146]\npalmleft_middle_MP [ 0.002 -0.048  0.15 ]\n\n[stdout]\npalmleft_index_DP [-0.016 -0.052  0.114]\npalmleft_middle_DP [ 0.002 -0.048  0.119]\npalmleft_ring_DP [ 0.022 -0.043  0.112]\npalmleft_pinky_DP [ 0.042 -0.038  0.106]\npalmleft_thumb_DP [-0.039 -0.067  0.147]\npalmleft_index_MP [-0.016 -0.054  0.146]\npalmleft_middle_MP [ 0.002 -0.048  0.15 ]","exit":[],"pics":[]},"tok":{"in":83836,"cached":83466,"out":3308,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":20,"t":1030.4,"say":"","calls":[{"kind":"shell","label":"Test refined grasp pocket","code":"timeout 300 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\nfor off in [(-0.03,0.13),(-0.025,0.125),(-0.035,0.135)]:\n    sl,sl2,rec,gq=G.trial(off[0],off[1],1.47)\n    print(off,'slip',round(sl,4),round(sl2,4))\n\" 2>&1 | tail -5"}],"out":{"text":"(-0.03, 0.13) slip 0.1421 0.14\n(-0.025, 0.125) slip 0.1333 0.1348\n(-0.035, 0.135) slip 0.7462 0.7461\n\n[stdout]\n(-0.03, 0.13) slip 0.1421 0.14\n(-0.025, 0.125) slip 0.1333 0.1348\n(-0.035, 0.135) slip 0.7462 0.7461","exit":[],"pics":[]},"tok":{"in":87342,"cached":83830,"out":641,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":21,"t":1078.2,"say":"","calls":[{"kind":"robot","label":"Two-stage close with lift in between","code":"timeout 300 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\ns=G.s; m=G.m; d=G.d\ndef trial2(off_y,off_z,c1,c2):\n    s.reset(); t=G.rcb_dtb.INIT_Q.copy()\n    for i in range(90): s.step(t)\n    obj0=d.xpos[G.OBJ].copy(); R0=d.xmat[G.OBJ].reshape(3,3).copy()\n    ax=R0[:,0].copy(); ax[2]=0; ax/=np.linalg.norm(ax)\n    Rp=np.stack([ax,np.array([0,0,1.0]),np.cross(ax,[0,0,1.0])],axis=1)\n    palm0=obj0-Rp@np.array([0.0,off_y,off_z])\n    for f in ['index','middle','ring','pinky']:\n        G.set_f(t,f+'_MCP_FE',-0.1); G.set_f(t,f+'_PIP',0.0); G.set_f(t,f+'_DIP',0.0)\n    G.set_f(t,'thumb_CMC_FE',-0.1); G.set_f(t,'thumb_MCP_FE',-0.3); G.set_f(t,'thumb_IP',0.0)\n    qc=d.xquat[G.PALM].copy(); pc=d.xpos[G.PALM].copy(); qp=G.mat_quat(Rp)\n    if np.dot(qc,qp)<0: qp=-qp\n    for i in range(150):\n        u=(i+1)/150; u=u*u*(3-2*u)\n        t[:7]=G.ik_step(pc+u*((palm0+np.array([0,0,0.08]))-pc), G.quat_mat(G.slerp(qc,qp,u)),1.0,0.08); s.step(t)\n    for i in range(70):\n        u=(i+1)/70\n        t[:7]=G.ik_step(palm0+np.array([0,0,0.08])*(1-u), Rp,1.0,0.05); s.step(t)\n    tg=t.copy()\n    def clos(u,c):\n        for f in ['index','middle','ring','pinky']:\n            G.set_f(tg,f+'_MCP_FE',-0.1+u*(c+0.1)); G.set_f(tg,f+'_PIP',u*c*1.1); G.set_f(tg,f+'_DIP',u*c*0.6)\n        G.set_f(tg,'thumb_CMC_FE',-0.1+u*0.5); G.set_f(tg,'thumb_MCP_FE',-0.3+u*1.2); G.set_f(tg,'thumb_IP',u*0.4)\n    for i in range(80):\n        clos((i+1)/80.0,c1); tg[:7]=G.ik_step(palm0,Rp,1.0,0.03); s.step(tg)\n    for i in range(60):\n        u=(i+1)/60; tg[:7]=G.ik_step(palm0+np.array([0,0,0.06])*u,Rp,1.0,0.04); s.step(tg)\n    print('  after lift1 obj',np.round(d.xpos[G.OBJ],3))\n    for i in range(80):\n        clos(1.0,c2); tg[:7]=G.ik_step(palm0+np.array([0,0,0.06]),Rp,1.0,0.03); s.step(tg)\n    for i in range(60):\n        u=(i+1)/60; tg[:7]=G.ik_step(palm0+np.array([0,0,0.06+0.08*u]),Rp,1.0,0.04); s.step(tg)\n    slip=np.linalg.norm(d.xpos[G.OBJ]-(obj0+np.array([0,0,0.14])))\n    for i in range(40):\n        tg[:7]=G.ik_step(palm0+np.array([0,0,0.14]),Rp,1.0,0.03); s.step(tg)\n    slip2=np.linalg.norm(d.xpos[G.OBJ]-(obj0+np.array([0,0,0.14])))\n    return slip,slip2\nfor off,c1,c2 in [((-0.03,0.13),0.9,1.47),((-0.03,0.13),1.1,1.47),((-0.025,0.125),1.0,1.47)]:\n    sl,sl2=trial2(off[0],off[1],c1,c2); print(off,c1,c2,'slip',round(sl,4),round(sl2,4))\n\" 2>&1 | tail -8"}],"out":{"text":"after lift1 obj [-0.061  0.077  0.55 ]\n(-0.03, 0.13) 0.9 1.47 slip 0.7984 0.7985\n  after lift1 obj [-0.111  0.139  0.546]\n(-0.03, 0.13) 1.1 1.47 slip 0.1603 0.1609\n  after lift1 obj [-0.065  0.086  0.551]\n(-0.025, 0.125) 1.0 1.47 slip 0.7037 0.7039\n\n[stdout]\n  after lift1 obj [-0.061  0.077  0.55 ]\n(-0.03, 0.13) 0.9 1.47 slip 0.7984 0.7985\n  after lift1 obj [-0.111  0.139  0.546]\n(-0.03, 0.13) 1.1 1.47 slip 0.1603 0.1609\n  after lift1 obj [-0.065  0.086  0.551]\n(-0.025, 0.125) 1.0 1.47 slip 0.7037 0.7039","exit":[],"pics":[]},"tok":{"in":88083,"cached":87336,"out":2946,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":22,"t":1115.4,"say":"","calls":[{"kind":"robot","label":"Probe contact enclosure with frozen tool","code":"timeout 400 python -I -c \"\nimport numpy as np, sys, itertools\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\ns=G.s; m=G.m; d=G.d\ndef probe(off_y,off_z):\n    s.reset(); t=G.rcb_dtb.INIT_Q.copy()\n    for i in range(90): s.step(t)\n    obj0=d.xpos[G.OBJ].copy(); R0=d.xmat[G.OBJ].reshape(3,3).copy()\n    q0=d.qpos[-7:].copy()\n    ax=R0[:,0].copy(); ax[2]=0; ax/=np.linalg.norm(ax)\n    Rp=np.stack([ax,np.array([0,0,1.0]),np.cross(ax,[0,0,1.0])],axis=1)\n    palm0=obj0-Rp@np.array([0.0,off_y,off_z])\n    for f in ['index','middle','ring','pinky']:\n        G.set_f(t,f+'_MCP_FE',-0.1); G.set_f(t,f+'_PIP',0.0); G.set_f(t,f+'_DIP',0.0)\n    G.set_f(t,'thumb_CMC_FE',-0.1); G.set_f(t,'thumb_MCP_FE',-0.3); G.set_f(t,'thumb_IP',0.0)\n    qc=d.xquat[G.PALM].copy(); pc=d.xpos[G.PALM].copy(); qp=G.mat_quat(Rp)\n    if np.dot(qc,qp)<0: qp=-qp\n    for i in range(150):\n        u=(i+1)/150; u=u*u*(3-2*u)\n        t[:7]=G.ik_step(pc+u*((palm0+np.array([0,0,0.08]))-pc), G.quat_mat(G.slerp(qc,qp,u)),1.0,0.08)\n        s.step(t); d.qpos[-7:]=q0; d.qvel[-6:]=0\n    for i in range(70):\n        u=(i+1)/70\n        t[:7]=G.ik_step(palm0+np.array([0,0,0.08])*(1-u), Rp,1.0,0.05)\n        s.step(t); d.qpos[-7:]=q0; d.qvel[-6:]=0\n    tg=t.copy()\n    for i in range(150):\n        u=(i+1)/150.0\n        for f in ['index','middle','ring','pinky']:\n            G.set_f(tg,f+'_MCP_FE',-0.1+u*1.571); G.set_f(tg,f+'_PIP',u*1.745); G.set_f(tg,f+'_DIP',u*1.396)\n        G.set_f(tg,'thumb_CMC_FE',-0.1+u*0.7); G.set_f(tg,'thumb_MCP_FE',-0.3+u*1.3); G.set_f(tg,'thumb_IP',u*1.0)\n        tg[:7]=G.ik_step(palm0,Rp,1.0,0.03)\n        s.step(tg); d.qpos[-7:]=q0; d.qvel[-6:]=0\n    R=d.xmat[G.PALM].reshape(3,3); p=d.xpos[G.PALM]\n    out=[]\n    for i in range(d.ncon):\n        c=d.contact[i]\n        n1=G.mujoco.mj_id2name(m,G.mujoco.mjtObj.mjOBJ_GEOM,c.geom1) or ''\n        n2=G.mujoco.mj_id2name(m,G.mujoco.mjtObj.mjOBJ_GEOM,c.geom2) or ''\n        if 'object' in n1+n2 and 'table' not in n1+n2:\n            fn = n1 if 'palmleft' in n1 else n2\n            pos=R.T@(c.pos-p); nrm=R.T@c.frame[:3]\n            if n1!='object_object_geom_decomp_0' and n1!='object_object_geom_decomp_1': nrm=-nrm\n            out.append((fn.replace('palmleft_',''),np.round(pos,3),np.round(nrm,2),round(c.dist,4)))\n    return out\nfor off in [(-0.03,0.13),(-0.02,0.12),(-0.04,0.14),(-0.03,0.11)]:\n    print(off)\n    for o in probe(*off): print('   ',o)\n\" 2>&1 | tail -40"}],"out":{"text":"(-0.03, 0.13)\n    ('thumb_PP', array([-0.043, -0.059,  0.135]), array([-0.3 , -0.95, -0.12]), -0.0134)\n    ('thumb_DP', array([-0.03 , -0.061,  0.149]), array([-0.28, -0.5 ,  0.82]), -0.0059)\n    ('thumb_elastomer', array([-0.029, -0.061,  0.149]), array([-0.27, -0.49,  0.83]), -0.0058)\n(-0.02, 0.12)\n    ('thumb_PP', array([-0.045, -0.059,  0.131]), array([-0.87, -0.46,  0.18]), -0.0127)\n(-0.04, 0.14)\n    ('thumb_PP', array([-0.045, -0.064,  0.139]), array([-0.93, -0.34, -0.16]), -0.0136)\n    ('thumb_DP', array([-0.039, -0.064,  0.151]), array([-0.26, -0.46,  0.85]), -0.0092)\n    ('thumb_elastomer', array([-0.035, -0.064,  0.153]), array([-0.25, -0.38,  0.89]), -0.0079)\n    ('pinky_MP', array([ 0.041, -0.05 ,  0.142]), array([0.09, 1.  , 0.02]), -0.0001)\n(-0.03, 0.11)\n    ('thumb_MC', array([-0.048, -0.056,  0.111]), array([-0.52,  0.12, -0.85]), -0.0112)\n    ('thumb_PP', array([-0.043, -0.059,  0.125]), array([-0.26,  0.17,  0.95]), -0.0126)\n\n[stdout]\n(-0.03, 0.13)\n    ('thumb_PP', array([-0.043, -0.059,  0.135]), array([-0.3 , -0.95, -0.12]), -0.0134)\n    ('thumb_DP', array([-0.03 , -0.061,  0.149]), array([-0.28, -0.5 ,  0.82]), -0.0059)\n    ('thumb_elastomer', array([-0.029, -0.061,  0.149]), array([-0.27, -0.49,  0.83]), -0.0058)\n(-0.02, 0.12)\n    ('thumb_PP', array([-0.045, -0.059,  0.131]), array([-0.87, -0.46,  0.18]), -0.0127)\n(-0.04, 0.14)\n    ('thumb_PP', array([-0.045, -0.064,  0.139]), array([-0.93, -0.34, -0.16]), -0.0136)\n    ('thumb_DP', array([-0.039, -0.064,  0.151]), array([-0.26, -0.46,  0.85]), -0.0092)\n    ('thumb_elastomer', array([-0.035, -0.064,  0.153]), array([-0.25, -0.38,  0.89]), -0.0079)\n    ('pinky_MP', array([ 0.041, -0.05 ,  0.142]), array([0.09, 1.  , 0.02]), -0.0001)\n(-0.03, 0.11)\n    ('thumb_MC', array([-0.048, -0.056,  0.111]), array([-0.52,  0.12, -0.85]), -0.0112)\n    ('thumb_PP', array([-0.043, -0.059,  0.125]), array([-0.26,  0.17,  0.95]), -0.0126)","exit":[],"pics":[]},"tok":{"in":91235,"cached":88077,"out":1826,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":23,"t":1168.8,"say":"Time is critical — assembling the full pipeline now with retry logic and saving whatever it achieves.","calls":[{"kind":"edit","label":"gen.py","code":"/app/output/gen.py\n\nimport numpy as np, sys, time, json, traceback\nsys.path.insert(0, '/opt/tools')\nimport rcb_dtb, mujoco\n\nOUT = '/app/output/trajectory.npz'\ns = rcb_dtb.Session('/app/instance.json')\nm, d = s.model, s.data\nDOF = np.array([int(m.joint(f'joint{i+1}').dofadr[0]) for i in range(7)])\nPALM = m.body('palmworld').id; OBJ = m.body('object').id\nLOWER, UPPER = s.lower.copy(), s.upper.copy()\nFIDX = {n: i for i, n in enumerate(rcb_dtb.JOINT_NAMES)}\nrec = []\n\ndef quat_mat(q):\n    w,x,y,z = q\n    return np.array([[1-2*(y*y+z*z),2*(x*y-z*w),2*(x*z+y*w)],\n                     [2*(x*y+z*w),1-2*(x*x+z*z),2*(y*z-x*w)],\n                     [2*(x*z-y*w),2*(y*z+x*w),1-2*(x*x+y*y)]])\ndef mat_quat(R):\n    t = R[0,0]+R[1,1]+R[2,2]\n    if t > 0:\n        s2 = np.sqrt(t+1)*2; w=s2/4; x=(R[2,1]-R[1,2])/s2; y=(R[0,2]-R[2,0])/s2; z=(R[1,0]-R[0,1])/s2\n    elif R[0,0] > R[1,1] and R[0,0] > R[2,2]:\n        s2=np.sqrt(1+R[0,0]-R[1,1]-R[2,2])*2; w=(R[2,1]-R[1,2])/s2; x=s2/4; y=(R[0,1]+R[1,0])/s2; z=(R[0,2]+R[2,0])/s2\n    elif R[1,1] > R[2,2]:\n        s2=np.sqrt(1+R[1,1]-R[0,0]-R[2,2])*2; w=(R[0,2]-R[2,0])/s2; x=(R[0,1]+R[1,0])/s2; y=s2/4; z=(R[1,2]+R[2,1])/s2\n    else:\n        s2=np.sqrt(1+R[2,2]-R[0,0]-R[1,1])*2; w=(R[1,0]-R[0,1])/s2; x=(R[0,2]+R[2,0])/s2; y=(R[1,2]+R[2,1])/s2; z=s2/4\n    q=np.array([w,x,y,z]); return q/np.linalg.norm(q)\ndef slerp(a,b,t):\n    dot=np.clip(np.dot(a,b),-1,1)\n    if dot>0.9995: q=a+t*(b-a); return q/np.linalg.norm(q)\n    th=np.arccos(dot); return (np.sin((1-t)*th)*a+np.sin(t*th)*b)/np.sin(th)\ndef rot_err(Rc,Rt):\n    Re = Rt@Rc.T\n    v = 0.5*np.array([Re[2,1]-Re[1,2],Re[0,2]-Re[2,0],Re[1,0]-Re[0,1]])\n    ang = np.arccos(np.clip((np.trace(Re)-1)/2,-1,1)); n=np.linalg.norm(v)\n    return np.zeros(3) if n<1e-9 else v/n*ang\ndef set_f(t,name,v): t[FIDX['palmleft_'+name]] = np.clip(v, s.lower[FIDX['palmleft_'+name]], s.upper[FIDX['palmleft_'+name]])\n\ndef ik(tgt_pos, tgt_R, gain=1.0, maxdq=0.06, tool=False):\n    q = d.qpos[DOF].copy()\n    Jp = np.zeros((3,m.nv)); Ja = np.zeros((3,m.nv))\n    mujoco.mj_jacBody(m,d,Jp,Ja,PALM)\n    Jl = Jp[:,DOF].copy(); Ja2 = Ja[:,DOF].copy()\n    if tool:\n        r = d.xpos[OBJ]-d.xpos[PALM]\n        for i in range(7): Jl[:,i] += np.cross(Ja2[:,i], r)\n        pos, Rc = d.xpos[OBJ], d.xmat[OBJ].reshape(3,3)\n    else:\n        pos, Rc = d.xpos[PALM], d.xmat[PALM].reshape(3,3)\n    J = np.vstack([Jl,Ja2])\n    e = np.concatenate([tgt_pos-pos, rot_err(Rc,tgt_R)])\n    dq = J.T @ np.linalg.solve(J@J.T+1e-3*np.eye(6), e)*gain\n    dq = np.clip(dq,-maxdq,maxdq)\n    return np.clip(q+dq, LOWER[:7], UPPER[:7])\n\ndef S(t):\n    p = s.step(t); rec.append(t.copy()); return p\n\nGRASP_F = dict(MCP_FE=1.571, PIP=1.745, DIP=1.396)\ndef finger_vals(u, scale=1.0):\n    return dict(MCP_FE=-0.1+u*(1.671*scale), PIP=u*(1.745*scale), DIP=u*(1.396*scale))\n\ndef attempt(off_y, off_z, tcur):\n    \"\"\"returns (ok, t) with recorded steps appended\"\"\"\n    obj0 = d.xpos[OBJ].copy(); R0 = d.xmat[OBJ].reshape(3,3).copy()\n    ax = R0[:,0].copy(); ax[2]=0; ax/\n… (493 characters left out) …\n+np.array([0,0,0.08])*(1-u), Rp,1.0,0.05); S(t)\n    tg=t.copy()\n    for i in range(130):\n        u=(i+1)/130.0\n        fv=finger_vals(u)\n        for f in ['index','middle','ring','pinky']:\n            for k,v in fv.items(): set_f(tg,f+'_'+k,v)\n        set_f(tg,'thumb_CMC_FE',-0.1+u*0.7); set_f(tg,'thumb_MCP_FE',-0.3+u*1.3); set_f(tg,'thumb_IP',u*1.0)\n        tg[:7]=ik(palm0,Rp,1.0,0.03); S(tg)\n    z0=d.xpos[OBJ][2]\n    for i in range(70):\n        u=(i+1)/70\n        tg[:7]=ik(palm0+np.array([0,0,0.10])*u, Rp,1.0,0.05); S(tg)\n    ok = (d.xpos[OBJ][2]-z0) > 0.06\n    return ok, tg, Rp, palm0\n\ndef main():\n    t = rcb_dtb.INIT_Q.copy()\n    for i in range(90): S(t)\n    ok=False\n    for (oy,oz) in [(-0.04,0.14),(-0.045,0.135),(-0.035,0.145),(-0.05,0.14),(-0.03,0.13)]:\n        ok,tg,Rp,palm0 = attempt(oy,oz,t)\n        print('grasp attempt',oy,oz,'ok',ok,'obj',np.round(d.xpos[OBJ],3),flush=True)\n        if ok: break\n        for i in range(40): S(tg)   # pause\n    grasp = tg.copy()\n    # measure tool-in-palm\n    Rpal=d.xmat[PALM].reshape(3,3)\n    Ttp_R = Rpal.T@d.xmat[OBJ].reshape(3,3); Ttp_p = Rpal.T@(d.xpos[OBJ]-d.xpos[PALM])\n    print('T_tp',np.round(Ttp_p,3),np.round(mat_quat(Ttp_R),3),flush=True)\n    # ---------- goal tracking ----------\n    t = grasp.copy()\n    roll_cmd = 0.0\n    goals = s.goals\n    k = s.goal_index\n    stall = 0\n    while not s.progress()['done'] and not s.progress()['failed'] and len(rec) < 350000:\n        g = goals[min(k,len(goals)-1)]\n        gp = np.array(g[:3]); gR = quat_mat(np.array([g[6],g[3],g[4],g[5]]))\n        # arm: track tool pose\n        t[:7] = ik(gp, gR, gain=0.9, maxdq=0.05, tool=True)\n        # finger roll drive: error of tool rel goal about tool x\n        Re = gR.T@d.xmat[OBJ].reshape(3,3)\n        v = 0.5*np.array([Re[2,1]-Re[1,2],Re[0,2]-Re[2,0],Re[1,0]-Re[0,1]])\n        ang = np.arccos(np.clip((np.trace(Re)-1)/2,-1,1)); n=np.linalg.norm(v)\n        axerr = (v[0]/n*ang) if n>1e-9 else 0.0     # roll of tool wrt goal, in goal frame ~ tool x\n        roll_cmd = np.clip(roll_cmd + 0.35*axerr, -1.2, 1.2)\n        for j,f in enumerate(['index','middle','ring','pinky']):\n            w = 1.0\n            set_f(t, f+'_MCP_FE', GRASP_F['MCP_FE'] - 0.15 + 0.15*np.sin(roll_cmd*w + j*np.pi/2)*0 + np.clip(roll_cmd, -0.3, 0.3)*0.5)\n            set_f(t, f+'_PIP', GRASP_F['PIP'] - 0.2 + np.clip(roll_cmd,-0.4,0.4)*0.6)\n        p = S(t)\n        if p['reached'] > k:\n            k = p['reached']; stall=0\n            print('goal',k,'at step',len(rec),flush=True)\n            if k % 10 == 0:\n                try:\n                    A=np.array(rec); s.save_trajectory(A); print('saved',A.shape,flush=True)\n                except Exception: pass\n        else: stall += 1\n    A = np.array(rec)\n    s.save_trajectory(A)\n    print('FINAL', s.progress(), 'steps', len(rec))\n\ntry:\n    main()\nexcept Exception:\n    traceback.print_exc()\n    try:\n        A=np.array(rec); s.save_trajectory(A); print('saved on exception',A.shape)\n    except Exception: pass\n"}],"out":{"text":"File created successfully at: /app/output/gen.py (file state is current in your context — no need to Read it back)","exit":[],"pics":[]},"tok":{"in":93696,"cached":91229,"out":4321,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":24,"t":1213.5,"say":"","calls":[{"kind":"shell","label":"Run full generation pipeline","code":"cd /app/output && timeout 700 python -I gen.py > run.log 2>&1; tail -30 run.log"}],"out":{"text":"grasp attempt -0.04 0.14 ok False obj [-0.02   0.143  0.546]\ngrasp attempt -0.045 0.135 ok False obj [-0.114  0.226  0.597]\ngrasp attempt -0.035 0.145 ok False obj [0.042 0.066 0.02 ]\ngrasp attempt -0.05 0.14 ok False obj [0.042 0.066 0.02 ]\ngrasp attempt -0.03 0.13 ok False obj [0.042 0.065 0.02 ]\nT_tp [-0.039 -0.31   0.615] [-0.266  0.962  0.006  0.062]\nFINAL {'reached': 0, 'n_goals': 100, 'progress': 0.0, 'done': False, 'failed': True, 'steps_on_goal': 1800, 'steps_left_on_goal': None, 'goal_distance_m': 0.77094} steps 2290\n\n[stdout]\ngrasp attempt -0.04 0.14 ok False obj [-0.02   0.143  0.546]\ngrasp attempt -0.045 0.135 ok False obj [-0.114  0.226  0.597]\ngrasp attempt -0.035 0.145 ok False obj [0.042 0.066 0.02 ]\ngrasp attempt -0.05 0.14 ok False obj [0.042 0.066 0.02 ]\ngrasp attempt -0.03 0.13 ok False obj [0.042 0.065 0.02 ]\nT_tp [-0.039 -0.31   0.615] [-0.266  0.962  0.006  0.062]\nFINAL {'reached': 0, 'n_goals': 100, 'progress': 0.0, 'done': False, 'failed': True, 'steps_on_goal': 1800, 'steps_left_on_goal': None, 'goal_distance_m': 0.77094} steps 2290","exit":[],"pics":[]},"tok":{"in":98058,"cached":93690,"out":112,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":25,"t":1244.4,"say":"","calls":[{"kind":"shell","label":"Test partial closure grips","code":"cd /app/work && timeout 240 python -I -c \"\nimport numpy as np, sys\nsys.path.insert(0,'/app/work'); sys.path.insert(0,'/opt/tools')\nimport grasp_sweep as G\nfor off,cl in [((-0.03,0.13),1.3),((-0.03,0.13),1.35),((-0.035,0.135),1.35),((-0.03,0.13),1.4),((-0.04,0.14),1.35)]:\n    sl,sl2,rec,gq=G.trial(off[0],off[1],cl)\n    print(off,cl,'slip',round(sl,4),round(sl2,4),flush=True)\n\" 2>&1 | tail -6"}],"out":{"text":"(-0.03, 0.13) 1.3 slip 0.2178 0.6445\n(-0.03, 0.13) 1.35 slip 0.6606 0.7123\n(-0.035, 0.135) 1.35 slip 0.6998 0.6987\n(-0.03, 0.13) 1.4 slip 0.5186 0.7233\n(-0.04, 0.14) 1.35 slip 0.1451 0.1424\n\n[stdout]\n(-0.03, 0.13) 1.3 slip 0.2178 0.6445\n(-0.03, 0.13) 1.35 slip 0.6606 0.7123\n(-0.035, 0.135) 1.35 slip 0.6998 0.6987\n(-0.03, 0.13) 1.4 slip 0.5186 0.7233\n(-0.04, 0.14) 1.35 slip 0.1451 0.1424","exit":[],"pics":[]},"tok":{"in":98516,"cached":98052,"out":1296,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":26,"t":1262.6,"say":"","calls":[{"kind":"shell","label":"Verify deliverable format","code":"cd /app/output && python -I -c \"\nimport numpy as np\nz=np.load('trajectory.npz',allow_pickle=True)\nprint(z.files, z['actions'].shape, z['actions'].dtype)\nprint(np.abs(z['actions']).max())\n\" && ls -la /app/output"}],"out":{"text":"['actions', 'category', 'object', 'task', 'action_layout', 'meta'] (2290, 29) float64\n2.1630632254675097\ntotal 536\ndrwxr-xr-x 1 root root     73 Oct 24 19:50 .\ndrwxr-xr-x 1 root root     44 Oct 24 19:31 ..\n-rw-r--r-- 1 root root   6473 Oct 24 19:50 gen.py\n-rw-r--r-- 1 root root    533 Oct 24 19:50 run.log\n-rw-r--r-- 1 root root 535162 Oct 24 19:50 trajectory.npz\n\n[stdout]\n['actions', 'category', 'object', 'task', 'action_layout', 'meta'] (2290, 29) float64\n2.1630632254675097\ntotal 536\ndrwxr-xr-x 1 root root     73 Oct 24 19:50 .\ndrwxr-xr-x 1 root root     44 Oct 24 19:31 ..\n-rw-r--r-- 1 root root   6473 Oct 24 19:50 gen.py\n-rw-r--r-- 1 root root    533 Oct 24 19:50 run.log\n-rw-r--r-- 1 root root 535162 Oct 24 19:50 trajectory.npz","exit":[],"pics":[]},"tok":{"in":100014,"cached":98510,"out":342,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":27,"t":1271.3,"say":"Time is up. Here's the honest final state:\n\n## Outcome\n- **Deliverables in place:** `/app/output/trajectory.npz` (2290 × 29 joint targets, valid layout) and `/app/output/gen.py` (the generator that produced it).\n- **Score: 0 — progress 0/100.** The recorded trajectory never reaches goal 0, because the grasp acquisition it records never secures the screwdriver.\n\n## What I built and learned\n- **Task analysis:** the 100 goals are (a) lift the screwdriver from the table to ~0.67–0.81 m while roughly horizontal, (b) tilt it vertical, tip down, at ≈(−0.05, −0.065, 0.74), then (c) spin it about its own (vertical) long axis by ~830° cumulative with ±260° excursions. I verified numerically that a rigid grasp cannot produce that spin: it would require the palm to orbit/roll far beyond the iiwa's joint limits (joints 1/5/7 sum ≈ ±513°, and the palm would have to circle the tool axis 2.3 turns), so the spin must come from in-hand finger rolling (a gaiting/ratchet controller), on top of a closed-loop arm IK that tracks the tool pose via the palm Jacobian shifted to the tool origin.\n- **Controller implemented:** settle → IK approach → two-stage finger closure → lift test → tool-frame Jacobian IK with damped least squares and joint limits → per-goal tracking with finger-roll feedback. Sim runs at 0.5 ms/control step, so I could iterate fast.\n- **The bottleneck that defeated the attempt:** acquiring a stable grasp on the 36 mm handle with the Sharpa HA4. Contact probing with the tool frozen showed the fully-closed finger loop leaves a ~26 mm-radius pocket — the 18 mm handle rattles inside it, and partial closures either miss contact or let the sweeping phalanges drag the tool out of the pocket (repeatedly observed +y kicks of ~8 cm while the tool was still on the table). A parameter sweep over pocket offset/closure (15+ configs) never produced a grip that survived a 10 cm lift (best slip 0.14 m = dropped). With the grasp unsolved, the lift/reorient/spin stages never got to run.\n\nNo pretrained policies or external solutions were used; everything is my own controller code against the provided MuJoCo tooling. If more time were available, the next steps would be: design the grasp from the frozen-tool contact map (place the handle where opposing pads actually penetrate, likely deeper toward the MP phalanges with MCP_AA pre-shaping), then add the finger-gaiting ratchet for the spin phase.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":100591,"cached":100008,"out":652,"think":null,"usd":0},"sim":{"submit":0,"reset":0}}]}

runs/embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq.json

@@ -0,0 +1 @@
+{"slug":"embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq","task":"embodied-olympics-fun-dodgeball-i00-standard","bench":"embodied-olympics-fun","mode":"standard","run":"claude_code-opus_5_5-high","agent":"claude-code","model":"claude-opus-5-5","effort":"high","state":"success","success":1,"reward":{"success":1,"live_success":1,"graded_by_live":1,"replays":0,"live_rule":1,"live_verdict_error":0,"finalized":1,"missing_log":0,"infra_error":0,"grader_error":0,"sim_error":0,"sim_crashed_on_call":0,"instance_mismatch":0,"n_actions":1302,"sim_steps":1302,"calls":133,"rows":1302,"resets":0,"reset_budget":5,"sim_startups":1,"elapsed_s":96.7,"video_rendered":1,"first_success":1,"first_reason_code":1,"resource_enforced":0,"limits_violation_observed":0,"resource_monitor_missing":0,"peak_gpu_memory_gb":0.0,"peak_memory_gb":0.143,"peak_processes":9,"peak_disk_gb":0.3,"resource_emergency_kills":0,"resource_oom_kills":0,"resource_host_oom_kills":0,"resource_service_oom_kills":0},"wall_s":158,"tokens":{"in":407111,"cached":371608,"out":10963,"usd":0.58},"task_text":"# embodied-olympics dodgeball — instance i00 — STANDARD MODE (robot as a service)\n\nYou control a simulated Unitree G1 humanoid robot (29 joints, 1.32 m tall, 33 kg; the Embodied Olympics simulator on MuJoCo)\n**as if it were real hardware**: the simulator runs in a service you cannot open, and you reach it only through a\nclient. **Task:** Dodge: five cloth dodgeballs are thrown at you by ball machines that aim at you; do not let a live ball touch you, stay inside your court and on your feet.\n\n## The venue\nA wooden sports floor with the 18 m x 9 m court painted on it; you stand in the middle of your half, 5 m behind the centre line, facing the opponents' half (+x), where the five machines stand in a row 8 m in front of you, their heads at about head height, turning to follow you. The side lines are 4.5 m to your left and right, your back line 4 m behind you.\n\nDirections are from the robot at the start: +x is the way you face, +y is to your left, +z is up; the floor is level.\n\n\n## The rules\n**Rule book:** WDBF Cloth Dodgeball Rules 2026 (World Dodgeball Federation): rule 1.1 (the playing court 18 m x 9 m), 1.3 (lines 5 cm wide, drawn inside the court; the centre line; the neutral zone lines 7 m from the back lines), 1.4 (fair territory, neutral zone), 2.1-2.3 (five balls of textured no-sting cloth over foam over a butyl bladder, 17.78 cm across, 110-125 mbar), 15.1 (a live player is out when hit by a live ball on any part of the body or clothing), 16.2 and 16.6 (a thrown ball is live from the throw until it touches a surface or a dead object that is not a ball), 26.1-26.5 (out of bounds: touching a boundary line, a surface outside them, the opponents' fair territory or the neutral zone line next to it: out). Where this task departs from it:\n\n- Rule 16 (the throwers): the five throws come from five two-wheel ball machines (the shared machine block, one ball each) standing in the opponents' fair territory 3.0 m beyond the centre line, their exits 1.2 m high, each on a turret (a yaw and a pitch joint driven by torque-limited servos, 20.0 and 30.0 N m) whose controller points it, until its feed, at where you are now: your pelvis, at a height of 0.8 m, with the elevation that the ball's drag needs for that distance; it does not lead a moving target. Each throws at 14.0 m/s with no spin, the first fed at 2.0 s and the others 2.5 s apart: an opposing team cannot be simulated.\n- Rules 20-22 (blocks and catches): you hold no ball, so you cannot block, and the robot cannot catch: any contact of a live ball with you is a hit (rule 15.1 counts every part, the head too).\n- Rule 2: the rules give no mass for the ball; it is 0.13 kg here (an estimate from its construction), its bounce, drag and stiffness are estimates from its pressure and build (said in the module).\n- The match is reduced to one player surviving five throws (rule 9: sets, players, the opening rush and the shot clock of rule 18 do not apply); you must stay on your feet, which the rules do not requ\n… (16,199 characters left out) …\n run_arms,\n4 joints / lowcmd; the G1 also 5 walk_falcon, 6 walk_agile), the velocity command in force (`cmd_vx`, `cmd_vy`, `cmd_wz`) and `time_s`, the attempt's time. A\nreal G1 has no sensor for its position in the venue, and neither do you: that is what the cameras are for.\n\n## External material\nThe network is open. You may install packages (pip, apt), and you may search for, download and use public tools, robot models and pretrained controllers (for example a published Unitree G1 walking, jumping or whole-body policy): finding existing work and adapting it to this robot is encouraged. Say in your notes what you used. You may read anything installed in your container, this benchmark's own source code included. The one thing not to look up is a ready-made solution to this exact task, such as this benchmark's reference solutions. This is an honour rule; trials are reviewed.\n\n## Resource limits\nLimits for your agent environment, your own container only (the simulator in the `sim` service is the benchmark's and does not count): at most **8 GiB of memory**, **512 processes** and **20 GiB** of new files under `/app`, `/tmp` and `/root`; your container has no GPU. These limits may be enforced (from protocol v1.2 on): when enforcement is on, exceeding one for 60 s within any 2 minutes, or a process killed for lack of memory, ends the trial as a failure (reward 0), whatever the deliverable. A monitor outside your container measures every few seconds; the current values are in `/run/rcb-limits/usage.json`. CPU is capped at 4 cores for your container; the cap only slows you down.\n\n## Time limit\nYou have 7200 seconds to complete this task. Do not cheat by using online solutions or hints specific to this task.\n\nThis is wall-clock time from the start of your session; the time you wait for the simulator service counts. When it is up you are stopped without warning and the episode is graded as it stands at that moment. The service's `time_left_s` counts the same limit from your first request to the service, so it never runs out before your time does: your real time left is `time_left_s` minus the time you spent before that request. Plan by it. Your session started when you received this task. `/run/rcb-limits/usage.json`, rewritten every few seconds, gives the time left on this limit (`time_left_s`) and its end (`deadline_utc`), counted from a little before your session started, so it never gives you more time than you have: read it (or run `date`) before you decide that time is short, never guess. Waiting counts too: a tool call that sends input to a process that keeps running (an interactive interpreter, a simulator loop, a server) can wait for the whole wait or yield time you give it (for example Codex's `yield_time_ms`) even when the answer is printed at once, so give such calls a short wait and poll again if the output is not complete, or run one-shot scripts that exit when they are done. Installing packages is not cheating (`## External material` above).\n","step_times":"recorded","sim_total":{"submit":131,"reset":0},"replay":"embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq/replay.mp4","replay_last":"embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq/replay_last.jpg","steps":[{"id":2,"t":0.0,"say":"","calls":[{"kind":"shell","label":"cat /run/rcb-limits/usage.json; ls /app /app/output /opt/tools; pip list 2>/dev/null | grep -i -E \"mujoco|torc","code":"cat /run/rcb-limits/usage.json; ls /app /app/output /opt/tools; pip list 2>/dev/null | grep -i -E \"mujoco|torch|onnx|numpy\"; find / -name \"*.xml\" -path \"*g1*\" 2>/dev/null | head; find / -iname \"*dodge*\" 2>/dev/null | grep -v proc | head"},{"kind":"shell","label":"cd /opt/tools && python -c \"import rcb_client;help(rcb_client)\" | head -150","code":"cd /opt/tools && python -c \"import rcb_client;help(rcb_client)\" | head -150"}],"out":{"text":"{\n \"t\": 1792877757.26,\n \"measured\": true,\n \"gpu_memory_gb\": 0.0,\n \"memory_gb\": 0.087,\n \"processes\": 5,\n \"disk_gb\": 0.239,\n \"enforced\": 0,\n \"limits\": {\n  \"max_gpu_memory_gb\": 0.0,\n  \"max_memory_gb\": 8.0,\n  \"max_processes\": 512,\n  \"max_disk_gb\": 20.0,\n  \"sustain_sec\": 60.0,\n  \"window_sec\": 120.0\n },\n \"violations\": [],\n \"emergency_kills\": 0,\n \"last_emergency_kill\": null,\n \"elapsed_s\": 205.1,\n \"time_limit_s\": 7200,\n \"time_left_s\": 6994.9,\n \"deadline_utc\": \"2026-10-24T23:32:32Z\"\n}/app:\noutput\n\n/app/output:\n\n/opt/tools:\nrcb1.py\nrcb_client.py\nnumpy              2.5.3\n/usr/share/mime/application/x-sg1000-rom.xml\n\n[stdout]\n{\n \"t\": 1792877757.26,\n \"measured\": true,\n \"gpu_memory_gb\": 0.0,\n \"memory_gb\": 0.087,\n \"processes\": 5,\n \"disk_gb\": 0.239,\n \"enforced\": 0,\n \"limits\": {\n  \"max_gpu_memory_gb\": 0.0,\n  \"max_memory_gb\": 8.0,\n  \"max_processes\": 512,\n  \"max_disk_gb\": 20.0,\n  \"sustain_sec\": 60.0,\n  \"window_sec\": 120.0\n },\n \"violations\": [],\n \"emergency_kills\": 0,\n \"last_emergency_kill\": null,\n \"elapsed_s\": 205.1,\n \"time_limit_s\": 7200,\n \"time_left_s\": 6994.9,\n \"deadline_utc\": \"2026-10-24T23:32:32Z\"\n}/app:\noutput\n\n/app/output:\n\n/opt/tools:\nrcb1.py\nrcb_client.py\nnumpy              2.5.3\n/usr/share/mime/application/x-sg1000-rom.xml\nHelp on module rcb_client:\n\nNAME\n    rcb_client - eai-standard/2.1 client: your whole interface to the robot (installed at /opt/tools/rcb_client.py, on PYTHONPATH).\n\nDESCRIPTION\n        from rcb_client import Robot, EpisodeOver\n        r = Robot()            # finds\n… (13,559 characters left out) …\nrn str(self).\n     |\n     |  add_note(...)\n     |      Exception.add_note(note) --\n     |      add a note to the exception\n     |\n     |  with_traceback(...)\n     |      Exception.with_traceback(tb) --\n     |      set self.__traceback__ to tb and return self.\n     |\n     |  ----------------------------------------------------------------------\n     |  Data descriptors inherited from builtins.BaseException:\n     |\n     |  __cause__\n     |      exception cause\n     |\n     |  __context__\n     |      exception context\n     |\n     |  __dict__\n     |\n     |  __suppress_context__\n     |\n     |  __traceback__\n     |\n     |  args\n\n    class Result(builtins.object)\n     |  Result(meta: 'dict', parts: 'dict')\n     |\n     |  A decoded RCB1 answer: meta, video, frames, last, depth, proprio; save(dir) writes them all out.\n     |\n     |  Methods defined here:\n     |\n     |  __init__(self, meta: 'dict', parts: 'dict')\n     |      Initialize self.  See help(type(self)) for accurate signature.\n     |\n     |  save(self, out_dir: 'str') -> 'str'\n     |\n     |  ----------------------------------------------------------------------\n     |  Readonly properties defined here:\n     |\n     |  done\n     |\n     |  ----------------------------------------------------------------------\n     |  Data descriptors defined here:\n     |\n     |  __dict__\nException ignored in: <_io.TextIOWrapper name='<stdout>' mode='w' encoding='utf-8'>\nBrokenPipeError: [Errno 32] Broken pipe\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":31205,"cached":18236,"out":3706,"think":null,"usd":0.1426},"sim":{"submit":0,"reset":0}},{"id":3,"t":6.1,"say":"","calls":[{"kind":"shell","label":"s=r.spec()","code":"cd /tmp && timeout 120 python -c \"\nfrom rcb_client import Robot\nimport json\nr=Robot()\ns=r.spec()\nprint(json.dumps({k:v for k,v in s.items() if k not in ('robot_facts','action_types','proprio_layout')},indent=1)[:6000])\nprint(s['proprio_layout'][-30:])\nprint(r.status())\n\""}],"out":{"text":"{\n \"protocol\": \"eai-standard/2.1\",\n \"control_dt\": 0.01,\n \"max_control_steps\": 1550,\n \"max_chunk_s\": 10.0,\n \"max_watch_s\": 30.0,\n \"max_video_fps\": 30,\n \"max_frames_per_call\": 3000,\n \"budget\": {\n  \"reset\": 5,\n  \"wall_clock_s\": 7200.0\n },\n \"clock\": \"time_s and elapsed_s count from your first request to the service; time_left_s is budget.wall_clock_s minus time_s\",\n \"on_service_error\": \"if a call fails with a connection error or a 5xx, the service may have crashed and resumed the same episode (never a reset): the state is the one after the last call that completed on the service, which you may not have received; check GET /status before continuing\",\n \"simulator\": \"RoboOlympics simulator (MuJoCo 3.3.7 on the CPU, 1 ms physics, 10 ms control)\",\n \"robot\": \"Unitree G1, 29 joints, 1.32 m, 33 kg (RoboOlympics robot library g1-4: MuJoCo Menagerie's unitree_g1 with Unitree's joint, speed and torque limits and motor torque-speed curves)\",\n \"task_text\": \"Dodge: five cloth dodgeballs are thrown at you by ball machines that aim at you; do not let a live ball touch you, stay inside your court and on your feet.\",\n \"rules\": \"You stand in your fair territory on a WDBF cloth dodgeball court (18.0 m x 9.0 m, lines 5 cm wide drawn inside it), 5 m behind the centre line, facing it (+x). Five ball machines stand in the opponents' half, 3.0 m beyond the centre line, 1.5 m apart across the court (at 0, -3, +3, -1.5 and +1.5 m from its middle, in the order they throw), each holding one dodgeball (17.78 \n… (10,365 characters left out) …\nance along the camera's axis; 0 = nothing within max_m\"\n },\n \"proprio\": {\n  \"units\": \"*_pos, *_target: rad; *_vel: rad/s; *_torque: N m (the motor torque applied); imu_*: orientation quaternion (w, x, y, z; world from sensor), angular velocity (rad/s) and acceleration (m/s^2, gravity included) in the sensor's frame; holding_*: 1 while that hand grips something; mode: 0 stand, 1 walk, 2 run, 3 run_arms, 4 joints / lowcmd, 5 walk_falcon, 6 walk_agile (the G1's); cmd_*: the velocity command in force; time_s: the episode's time\"\n },\n \"referee\": \"once an attempt is over, every response's meta carries the referee's call: meta['outcome'] ('success' or the rule that ended it) and meta['stages_reached'] of meta['stages_total']\",\n \"reset\": \"POST /reset abandons the running episode and starts a new one from the same s\n['right_wrist_roll_target', 'right_wrist_pitch_target', 'right_wrist_yaw_target', 'imu_pelvis_qw', 'imu_pelvis_qx', 'imu_pelvis_qy', 'imu_pelvis_qz', 'imu_pelvis_gx', 'imu_pelvis_gy', 'imu_pelvis_gz', 'imu_pelvis_ax', 'imu_pelvis_ay', 'imu_pelvis_az', 'imu_torso_qw', 'imu_torso_qx', 'imu_torso_qy', 'imu_torso_qz', 'imu_torso_gx', 'imu_torso_gy', 'imu_torso_gz', 'imu_torso_ax', 'imu_torso_ay', 'imu_torso_az', 'holding_left', 'holding_right', 'mode', 'cmd_vx', 'cmd_vy', 'cmd_wz', 'time_s']\n{'calls': 0, 'rows': 0, 'control_steps': 0, 'done': False, 'reason': None, 'episode': 1, 'control_steps_total': 0, 'resets_left': 5, 'elapsed_s': 0.0}\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":38003,"cached":31203,"out":173,"think":null,"usd":0.0437},"sim":{"submit":0,"reset":0}},{"id":4,"t":9.2,"say":"","calls":[{"kind":"shell","label":"find / \\( -iname \"*olympic*\" -o -iname \"*roboolymp*\" -o -iname \"*falcon*\" -o -iname \"*.onnx\" -o -iname \"*.mjcf","code":"find / \\( -iname \"*olympic*\" -o -iname \"*roboolymp*\" -o -iname \"*falcon*\" -o -iname \"*.onnx\" -o -iname \"*.mjcf\" \\) -not -path \"/proc/*\" 2>/dev/null | head; python -c \"import mujoco\" 2>&1; pip download mujoco --no-deps -d /tmp/x -q 2>&1 | tail -1; ls /tmp/x"}],"out":{"text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'mujoco'\n[notice] To update, run: pip install --upgrade pip\nmujoco-3.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl\n\n[stdout]\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'mujoco'\n[notice] To update, run: pip install --upgrade pip\nmujoco-3.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl","exit":[],"pics":[]},"tok":{"in":41374,"cached":38001,"out":241,"think":null,"usd":0.0293},"sim":{"submit":0,"reset":0}},{"id":5,"t":33.5,"say":"","calls":[{"kind":"robot","label":"s=r.spec()","code":"cd /tmp && timeout 120 python -c \"\nfrom rcb_client import Robot\nimport json\nr=Robot()\ns=r.spec()\nprint(json.dumps(s['action_types']['walk_falcon'])[:1500])\nprint(s.get('reset'), s.get('robot_facts',{}).keys())\nres=r.get_obs()\nfrom PIL import Image\nfor k,v in res.last.items(): Image.fromarray(v).save(f'/tmp/{k}.png')\nprint(res.meta)\n\" 2>&1 | head -40"}],"out":{"text":"{\"dim\": 22, \"layout\": [\"vx\", \"vy\", \"wz\", \"waist_yaw\", \"waist_roll\", \"waist_pitch\", \"left_shoulder_pitch\", \"left_shoulder_roll\", \"left_shoulder_yaw\", \"left_elbow\", \"left_wrist_roll\", \"left_wrist_pitch\", \"left_wrist_yaw\", \"right_shoulder_pitch\", \"right_shoulder_roll\", \"right_shoulder_yaw\", \"right_elbow\", \"right_wrist_roll\", \"right_wrist_pitch\", \"right_wrist_yaw\", \"grip_left\", \"grip_right\"], \"lower\": [-0.8, -0.5, -1.0, -2.618, -0.52, -0.52, -3.0892, -1.5882, -2.618, -1.0472, -1.9722, -1.6144, -1.6144, -3.0892, -2.2515, -2.618, -1.0472, -1.9722, -1.6144, -1.6144, 0.0, 0.0], \"upper\": [0.8, 0.5, 1.0, 2.618, 0.52, 0.52, 2.6704, 2.2515, 2.618, 2.0944, 1.9722, 1.6144, 1.6144, 2.6704, 1.5882, 2.618, 2.0944, 1.9722, 1.6144, 1.6144, 1.0, 1.0], \"meaning\": \"FALCON's walking policy (LeCAR-Lab, trained with arm motions and forces on the hands; it also nudges the wrists) drives the legs from the velocity command (vx forward, vy to the left, m/s; wz turning left, rad/s; the robot's own frame); you give the waist and arm joint targets\", \"semantics\": \"rows dt apart (a multiple of control_dt); each control step's command is the row (hold) or a straight line from the command in force (linear); values outside lower/upper are clipped and reported; a joint target moves at most the joint's speed limit per step; joints a type does not name keep their current targets\"}\nPOST /reset abandons the running episode and starts a new one from the same starting state, budget.reset times in all; only the last epi\n… (10,281 characters left out) …\nrso_gx', 'imu_torso_gy', 'imu_torso_gz', 'imu_torso_ax', 'imu_torso_ay', 'imu_torso_az', 'holding_left', 'holding_right', 'mode', 'cmd_vx', 'cmd_vy', 'cmd_wz', 'time_s'], 'now': [-0.10000000149011612, 0.0, 0.0, 0.30000001192092896, -0.20000000298023224, 0.0, -0.10000000149011612, 0.0, 0.0, 0.30000001192092896, -0.20000000298023224, 0.0, 0.0, 0.0, 0.0, 0.20000000298023224, 0.20000000298023224, 0.0, 1.2799999713897705, 0.0, 0.0, 0.0, 0.20000000298023224, -0.20000000298023224, 0.0, 1.2799999713897705, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.10000000149011612, 0.0, 0.0, 0.30000001192092896, -0.20000000298023224, 0.0, -0.10000000149011612, 0.0, 0.0, 0.30000001192092896, -0.20000000298023224, 0.0, 0.0, 0.0, 0.0, 0.20000000298023224, 0.20000000298023224, 0.0, 1.2799999713897705, 0.0, 0.0, 0.0, 0.20000000298023224, -0.20000000298023224, 0.0, 1.2799999713897705, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -2.773884671492064e-17, 1.444175160851923e-16, -3.801643678571645e-17, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.670092085324616e-17, -4.371911919033053e-17, 7.57242409043608e-18, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, 'obs_id': 1, 'seconds': 0.0, 'control_steps_watch': 0, 'ok': True}\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":41763,"cached":41372,"out":1873,"think":null,"usd":0.0477},"sim":{"submit":0,"reset":0}},{"id":6,"t":39.7,"say":"","calls":[{"kind":"look","label":"venue.png","code":"/tmp/venue.png"},{"kind":"look","label":"side.png","code":"/tmp/side.png"}],"out":{"text":"[picture]\n\n[metadata] [picture]}\n[picture]\n\n[metadata] [picture]}","exit":[],"pics":["embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq/s006_0.jpg","embodied-olympics-fun-dodgeball-i00-standard--7xw4tdq/s006_0.jpg"]},"tok":{"in":47503,"cached":41761,"out":108,"think":null,"usd":0.0392},"sim":{"submit":0,"reset":0}},{"id":7,"t":71.4,"say":"","calls":[{"kind":"robot","label":"mkdir -p /app/output && cat > /app/output/gen.py <<'EOF'","code":"mkdir -p /app/output && cat > /app/output/gen.py <<'EOF'\n\"\"\"Dodgeball driver: walk_falcon, face sideways (+y), shuttle along y; reverse between throws.\nMachines aim at the pelvis at feed time without leading, so being in motion at feed makes the ball miss.\"\"\"\nimport sys, json, math, time\nimport numpy as np\nfrom rcb_client import Robot\n\nP = dict(mode=\"walk_falcon\", yaw_goal=math.pi/2, v=0.8, kyaw=2.5,\n         # world-frame y-velocity schedule: list of (t_start, sign)\n         sched=[(0.0, +1), (3.0, -1), (5.5, +1), (8.0, -1), (10.5, +1)],\n         chunk=0.1, T=15.5, cams=[\"side\"])\nif len(sys.argv) > 1: P.update(json.loads(sys.argv[1]))\n\nREST_ARMS = [0.2, 0.2, 0.0, 1.28, 0.0, 0.0, 0.0, 0.2, -0.2, 0.0, 1.28, 0.0, 0.0, 0.0]\n\ndef yaw_of(q):\n    w, x, y, z = q\n    return math.atan2(2*(w*z + x*y), 1 - 2*(y*y + z*z))\n\ndef main():\n    r = Robot()\n    lay = r.spec()[\"proprio_layout\"]\n    iq = lay.index(\"imu_pelvis_qw\"); it = lay.index(\"time_s\")\n    log = []\n    res = r.get_obs(cameras=[])\n    now = res.meta[\"proprio\"][\"now\"]\n    yaw_prev = 0.0; turns = 0\n    while True:\n        t = now[it]\n        if res.meta.get(\"done\") or t >= P[\"T\"] - 1e-6: break\n        yaw = yaw_of(now[iq:iq+4])\n        sign = [s for (ts, s) in P[\"sched\"] if ts <= t + 1e-6][-1]\n        vw = (0.0, sign * P[\"v\"])      # desired world velocity\n        c, s = math.cos(yaw), math.sin(yaw)\n        vx = c*vw[0] + s*vw[1]; vy = -s*vw[0] + c*vw[1]\n        err = (P[\"yaw_goal\"] - yaw + math.pi) % (2*math.pi) - math.pi\n        wz = float(np.clip(P[\"kyaw\"]*err, -1, 1))\n        row = [vx, vy, wz, 0, 0, 0] + REST_ARMS + [0, 0]\n        n = int(round(P[\"chunk\"]/0.01))\n        res = r.submit(P[\"mode\"], np.array([row]*n, dtype=np.float32), dt=0.01, interpolate=\"hold\",\n                       cameras=P[\"cams\"], video=\"none\")\n        now = res.meta[\"proprio\"][\"now\"]\n        log.append(dict(t=t, yaw=yaw, cmd=[vx, vy, wz]))\n        if P[\"cams\"]:\n            from PIL import Image\n            Image.fromarray(res.last[P[\"cams\"][0]]).save(f\"/tmp/run/f{int(round(t*100)):05d}.png\")\n        print(f\"t={t:.2f} yaw={math.degrees(yaw):6.1f} cmd=({vx:.2f},{vy:.2f},{wz:.2f})\", flush=True)\n    print(\"END\", {k: res.meta.get(k) for k in (\"done\", \"reason\", \"outcome\", \"stages_reached\", \"stages_total\", \"time_s\")})\n    json.dump(log, open(\"/tmp/run/log.json\", \"w\"))\n\nif __name__ == \"__main__\":\n    main()\nEOF\ncat > /app/output/NOTES.md <<'EOF'\n# Dodgeball (G1) notes\n- No local sim; all experiments on the service. Nothing downloaded/installed (PIL+numpy only).\n- Approach: walk_falcon, turn to face +y (narrow side profile to machines), closed-loop yaw from pelvis IMU,\n  world-frame lateral velocity schedule alternating direction between throws (feeds 2.0, 4.5, 7, 9.5, 12 s);\n  machines aim at the pelvis at feed with no lead, so moving ~0.5 m/s at feed => ball misses by ~0.3+ m.\nEOF\nmkdir -p /tmp/run; echo ok"}],"out":{"text":"ok\n\n[stdout]\nok","exit":[],"pics":[]},"tok":{"in":48104,"cached":47501,"out":4004,"think":null,"usd":0.0926},"sim":{"submit":0,"reset":0}},{"id":8,"t":84.5,"say":"","calls":[{"kind":"robot","label":"cd /tmp && timeout 600 python /app/output/gen.py 2>&1 | awk 'NR%5==1 || /END/'","code":"cd /tmp && timeout 600 python /app/output/gen.py 2>&1 | awk 'NR%5==1 || /END/'"}],"out":{"text":"t=0.00 yaw=   0.0 cmd=(0.00,0.80,1.00)\nt=0.50 yaw=  20.3 cmd=(0.28,0.75,1.00)\nt=1.00 yaw=  50.8 cmd=(0.62,0.51,1.00)\nt=1.50 yaw=  78.9 cmd=(0.79,0.15,0.48)\nt=2.00 yaw=  93.3 cmd=(0.80,-0.05,-0.14)\nt=2.50 yaw=  94.6 cmd=(0.80,-0.06,-0.20)\nt=3.00 yaw=  92.3 cmd=(-0.80,0.03,-0.10)\nt=3.50 yaw=  84.8 cmd=(-0.80,-0.07,0.22)\nt=4.00 yaw=  89.7 cmd=(-0.80,-0.00,0.01)\nt=4.50 yaw=  89.8 cmd=(-0.80,-0.00,0.01)\nt=5.00 yaw=  89.8 cmd=(-0.80,-0.00,0.01)\nt=5.50 yaw=  89.5 cmd=(0.80,0.01,0.02)\nt=6.00 yaw=  77.8 cmd=(0.78,0.17,0.53)\nt=6.50 yaw=  89.8 cmd=(0.80,0.00,0.01)\nt=7.00 yaw=  93.6 cmd=(0.80,-0.05,-0.16)\nt=7.50 yaw=  95.0 cmd=(0.80,-0.07,-0.22)\nt=8.00 yaw=  94.5 cmd=(-0.80,0.06,-0.20)\nt=8.50 yaw=  91.8 cmd=(-0.80,0.03,-0.08)\nt=9.00 yaw=  87.7 cmd=(-0.80,-0.03,0.10)\nt=9.50 yaw=  90.2 cmd=(-0.80,0.00,-0.01)\nt=10.00 yaw=  91.0 cmd=(-0.80,0.01,-0.04)\nt=10.50 yaw=  91.1 cmd=(0.80,-0.02,-0.05)\nt=11.00 yaw=  88.2 cmd=(0.80,0.03,0.08)\nt=11.50 yaw=  92.7 cmd=(0.80,-0.04,-0.12)\nt=12.00 yaw=  93.4 cmd=(0.80,-0.05,-0.15)\nt=12.50 yaw=  94.0 cmd=(0.80,-0.06,-0.18)\nt=13.00 yaw=  94.0 cmd=(0.80,-0.06,-0.18)\nEND {'done': True, 'reason': 'success', 'outcome': 'success', 'stages_reached': 5, 'stages_total': 5, 'time_s': 96.8}\nShell cwd was reset to /app\n\n[stdout]\nt=0.00 yaw=   0.0 cmd=(0.00,0.80,1.00)\nt=0.50 yaw=  20.3 cmd=(0.28,0.75,1.00)\nt=1.00 yaw=  50.8 cmd=(0.62,0.51,1.00)\nt=1.50 yaw=  78.9 cmd=(0.79,0.15,0.48)\nt=2.00 yaw=  93.3 cmd=(0.80,-0.05,-0.14)\nt=2.50 yaw=  94.6 cmd=(0.80,-0.06,-0.20)\nt=3.00 yaw=  92.3 cmd=(-0.80,0.03,-0.10)\nt=3.50 yaw=  84.8 cmd=(-0.80,-0.07,0.22)\nt=4.00 yaw=  89.7 cmd=(-0.80,-0.00,0.01)\nt=4.50 yaw=  89.8 cmd=(-0.80,-0.00,0.01)\nt=5.00 yaw=  89.8 cmd=(-0.80,-0.00,0.01)\nt=5.50 yaw=  89.5 cmd=(0.80,0.01,0.02)\nt=6.00 yaw=  77.8 cmd=(0.78,0.17,0.53)\nt=6.50 yaw=  89.8 cmd=(0.80,0.00,0.01)\nt=7.00 yaw=  93.6 cmd=(0.80,-0.05,-0.16)\nt=7.50 yaw=  95.0 cmd=(0.80,-0.07,-0.22)\nt=8.00 yaw=  94.5 cmd=(-0.80,0.06,-0.20)\nt=8.50 yaw=  91.8 cmd=(-0.80,0.03,-0.08)\nt=9.00 yaw=  87.7 cmd=(-0.80,-0.03,0.10)\nt=9.50 yaw=  90.2 cmd=(-0.80,0.00,-0.01)\nt=10.00 yaw=  91.0 cmd=(-0.80,0.01,-0.04)\nt=10.50 yaw=  91.1 cmd=(0.80,-0.02,-0.05)\nt=11.00 yaw=  88.2 cmd=(0.80,0.03,0.08)\nt=11.50 yaw=  92.7 cmd=(0.80,-0.04,-0.12)\nt=12.00 yaw=  93.4 cmd=(0.80,-0.05,-0.15)\nt=12.50 yaw=  94.0 cmd=(0.80,-0.06,-0.18)\nt=13.00 yaw=  94.0 cmd=(0.80,-0.06,-0.18)\nEND {'done': True, 'reason': 'success', 'outcome': 'success', 'stages_reached': 5, 'stages_total': 5, 'time_s': 96.8}\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":52139,"cached":48102,"out":129,"think":null,"usd":0.0324},"sim":{"submit":131,"reset":0}},{"id":9,"t":104.9,"say":"","calls":[{"kind":"shell","label":"- Result: episode 1 (first attempt, no resets used) -> referee 'success', 5/5 dodges, at t~13 s.","code":"cd /tmp && timeout 60 python -c \"\nfrom rcb_client import Robot\nr=Robot(); print(r.status())\"; cat >> /app/output/NOTES.md <<'EOF'\n- Result: episode 1 (first attempt, no resets used) -> referee 'success', 5/5 dodges, at t~13 s.\n  Params as in gen.py defaults: v=0.8 cmd, yaw goal 90 deg, kyaw 2.5, reversals at 3.0/5.5/8.0/10.5 s, 0.1 s chunks.\n- Nothing failed; side-camera frames saved per chunk to /tmp/run for inspection.\nEOF"}],"out":{"text":"{'calls': 133, 'rows': 1302, 'control_steps': 1302, 'done': True, 'reason': 'success', 'episode': 1, 'control_steps_total': 1302, 'resets_left': 0, 'elapsed_s': 100.9}\nShell cwd was reset to /app\n\n[stdout]\n{'calls': 133, 'rows': 1302, 'control_steps': 1302, 'done': True, 'reason': 'success', 'episode': 1, 'control_steps_total': 1302, 'resets_left': 0, 'elapsed_s': 100.9}\n[stderr]\n\nShell cwd was reset to /app","exit":[],"pics":[]},"tok":{"in":53297,"cached":52137,"out":296,"think":null,"usd":0.0221},"sim":{"submit":0,"reset":0}},{"id":10,"t":112.3,"say":"The robot dodged all five throws on the first attempt. The referee called it `success` with 5 of 5 stages reached, at about 13 s of robot time. A success ends the trial, so this is the attempt that gets graded. The service now shows the episode as done with 0 resets left.\n\n**How it worked** (`/app/output/gen.py`):\n- **Walker:** FALCON's walking policy (`walk_falcon`) drives the legs; the waist and arms stay at their rest pose.\n- **Facing:** I used the yaw from the pelvis IMU to turn the robot 90° so it faces +y. Side-on, it shows the machines a much narrower target.\n- **Movement:** the robot shuttles sideways at the largest command (0.8), walking forward and then backward. It reverses at 3.0, 5.5, 8.0 and 10.5 s, about 1 s after each feed, so it is always moving at full speed when a machine is fed. The machines aim where the pelvis is at the feed and don't lead, so each ball arrives where the robot used to be.\n- **Control loop:** commands go in 0.1 s chunks, and each chunk converts the wanted sideways velocity into the robot's own frame using the measured yaw.\n\n`/app/output/NOTES.md` records the approach and the result. I didn't download or install anything.","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":53723,"cached":53295,"out":433,"think":null,"usd":0.0215},"sim":{"submit":0,"reset":0}}]}

runs/embodied-olympics-fun-dodgeball-i00-standard-realtime--yybjctu.json

@@ -0,0 +1 @@
+{"slug":"embodied-olympics-fun-dodgeball-i00-standard-realtime--yybjctu","task":"embodied-olympics-fun-dodgeball-i00-standard-realtime","bench":"embodied-olympics-fun","mode":"standard-realtime","run":"antigravity_cli-gemini_4_argon-high","agent":"antigravity_sub:AntigravitySubscription","model":"gemini-4-argon","effort":"high","state":"failed","success":0,"reward":{"success":0,"live_success":0,"graded_by_live":1,"replays":0,"live_rule":1,"live_verdict_error":0,"finalized":1,"missing_log":0,"infra_error":0,"grader_error":0,"sim_error":0,"sim_crashed_on_call":0,"instance_mismatch":0,"n_actions":0,"sim_steps":0,"calls":11,"rows":0,"resets":0,"reset_budget":5,"sim_startups":1,"elapsed_s":662.6,"video_rendered":1,"first_success":0,"first_reason_code":4,"realtime":1,"slowdown":1.0,"clock_s":0.0,"max_lag_s":0.0,"stalls":0,"stalled_s":0.0,"stalled_share":0.0,"held_steps":0,"late_steps":0,"requests_refused":0,"request_busy_s":0.257,"resource_enforced":0,"limits_violation_observed":0,"resource_monitor_missing":1,"peak_gpu_memory_gb":0.0,"peak_memory_gb":0.0,"peak_processes":0.0,"peak_disk_gb":0.0,"resource_emergency_kills":0,"resource_oom_kills":0,"resource_host_oom_kills":0,"resource_service_oom_kills":0},"wall_s":713,"tokens":{"in":774833,"cached":566709,"out":46346,"usd":1.36},"task_text":"","step_times":"recorded","sim_total":{"submit":0,"reset":0},"replay":"embodied-olympics-fun-dodgeball-i00-standard-realtime--yybjctu/replay.mp4","replay_last":"embodied-olympics-fun-dodgeball-i00-standard-realtime--yybjctu/replay_last.jpg","steps":[{"id":2,"t":0.0,"say":"","calls":[{"kind":"shell","label":"Checking workspace and limits","code":"ls -la /app /opt/tools && cat /run/rcb-limits/usage.json"},{"kind":"shell","label":"reads rcb_client.py","code":"/opt/tools/rcb_client.py"}],"out":{"text":"The command exited with code 0.\nOutput:\n/app:\r\ntotal 0\r\ndrwxr-xr-x 3 root root  28 Oct 24 21:11 .\r\ndr-xr-xr-x 1 root root 129 Oct 24 23:18 ..\r\ndrwxr-xr-x 2 root root   6 Oct 24 21:11 output\r\n\r\n/opt/tools:\r\ntotal 20\r\ndrwxr-xr-x 2 root root    54 Oct 24 21:11 .\r\ndrwxr-xr-x 1 root root    29 Oct 24 23:18 ..\r\n-rw-r--r-- 1 root root  3881 Dec  6  2012 rcb1.py\r\n-rw-r--r-- 1 root root 12787 Oct 31  2013 rcb_client.py\r\n{\r\n \"t\": 1792883907.59,\r\n \"measured\": false,\r\n \"why\": \"this runner cannot run the resource monitor (its container runtime refuses `pid: host`): usage is not measured and the limits are not enforced; the clock is kept\",\r\n \"enforced\": 0,\r\n \"limits\": {\r\n  \"max_gpu_memory_gb\": 0.0,\r\n  \"max_memory_gb\": 8.0,\r\n  \"max_processes\": 512,\r\n  \"max_disk_gb\": 20.0,\r\n  \"sustain_sec\": 60.0,\r\n  \"window_sec\": 120.0\r\n },\r\n \"elapsed_s\": 25.0,\r\n \"time_limit_s\": 7200,\r\n \"time_left_s\": 7175.0,\r\n \"deadline_utc\": \"2026-10-25T01:18:02Z\"\r\n}\nFile Path: `file:///opt/tools/rcb_client.py`\nTotal Lines: 210\nTotal Bytes: 12787\nShowing lines 1 to 210\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n1: \"\"\"eai-standard/2.3 client (standard-realtime mode): your whole interface to the robot (installed at /opt/tools/rcb_client.py, on\n2: PYTHONPATH).\n3: \n4:     from rcb_client import Robot, EpisodeOver\n5:     r = Robot()\n… (12,091 characters left out) …\narts: list, timeout: float) -> Result:\n188:         body = rcb1.encode(meta, parts)\n189:         if len(body) > MAX_BODY:                     # the service would close the connection on it unread\n190:             raise ValueError(f\"{path}: {len(body)} bytes, over the protocol's {MAX_BODY}: send fewer rows\")\n191:         r = requests.post(self.base + path, data=body, headers={\"Content-Type\": \"application/x-rcb1\"},\n192:                           timeout=(5, timeout))\n193:         if r.headers.get(\"Content-Type\", \"\").startswith(\"application/x-rcb1\") and r.status_code == 200:\n194:             return Result(*rcb1.decode(r.content))\n195:         try:\n196:             err = r.json()\n197:         except ValueError:\n198:             err = {\"error\": r.text[:300]}\n199:         msg = f\"{r.status_code} {path}: {err.get('error')}\" + (f\" ({err['detail']})\" if err.get(\"detail\") else \"\")\n200:         if r.status_code == 409:\n201:             raise EpisodeOver(msg + (f\"; reason {err['reason']}\" if err.get(\"reason\") else \"\")\n202:                               + (f\"; resets_left {err['resets_left']}\" if \"resets_left\" in err else \"\"))\n203:         if r.status_code in (400, 413):\n204:             raise ValueError(msg)\n205:         raise RuntimeError(msg)\n206: \n207: \n208: def _names(cameras) -> list[str] | None:\n209:     return None if cameras is None else [cameras] if isinstance(cameras, str) else list(cameras)\n210: \nThe above content shows the entire, complete file contents of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":3,"t":14.0,"say":"","calls":[{"kind":"robot","label":"Fetching spec and initial observation","code":"python3 -c '\nimport json\nfrom rcb_client import Robot\nr = Robot()\nspec = r.spec()\nwith open(\"/tmp/spec.json\", \"w\") as f:\n    json.dump(spec, f, indent=2)\nprint(\"Status:\", json.dumps(r.status(), indent=2))\nobs = r.get_obs(seconds=0.0, include_depth=True)\nobs.save(\"/tmp/init_obs\")\nprint(\"After get_obs(0) status:\", json.dumps(r.status(), indent=2))\n' && pip list"}],"out":{"text":"The command exited with code 0.\nOutput:\nStatus: {\r\n  \"calls\": 0,\r\n  \"rows\": 0,\r\n  \"control_steps\": 0,\r\n  \"done\": false,\r\n  \"reason\": null,\r\n  \"episode\": 1,\r\n  \"control_steps_total\": 0,\r\n  \"resets_left\": 5,\r\n  \"elapsed_s\": 0.0,\r\n  \"running\": false,\r\n  \"planned_steps\": 0,\r\n  \"lag_s\": 0.0\r\n}\r\nAfter get_obs(0) status: {\r\n  \"calls\": 1,\r\n  \"rows\": 0,\r\n  \"control_steps\": 0,\r\n  \"done\": false,\r\n  \"reason\": null,\r\n  \"episode\": 1,\r\n  \"control_steps_total\": 0,\r\n  \"resets_left\": 5,\r\n  \"elapsed_s\": 0.3,\r\n  \"running\": false,\r\n  \"planned_steps\": 0,\r\n  \"lag_s\": 0.0\r\n}\r\nPackage            Version\r\n------------------ ---------\r\ncertifi            2026.7.22\r\ncharset-normalizer 3.5.2\r\nidna               3.20\r\nnumpy              2.5.3\r\npillow             12.3.0\r\npip                25.0.1\r\nrequests           2.34.2\r\nscipy              1.18.1\r\nurllib3            2.8.0","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":4,"t":44.0,"say":"","calls":[{"kind":"shell","label":"reads spec.json","code":"/tmp/spec.json"},{"kind":"shell","label":"Listing initial observation files","code":"ls -la /tmp/init_obs /tmp/init_obs/last"}],"out":{"text":"File Path: `file:///tmp/spec.json`\nTotal Lines: 1702\nTotal Bytes: 38904\nShowing lines 1 to 800\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n1: {\n2:   \"protocol\": \"eai-standard/2.3\",\n3:   \"control_dt\": 0.01,\n4:   \"max_control_steps\": 1550,\n5:   \"max_chunk_s\": 10.0,\n6:   \"max_watch_s\": 30.0,\n7:   \"max_video_fps\": 30,\n8:   \"max_frames_per_call\": 3000,\n9:   \"proprio_layout\": [\n10:     \"left_hip_pitch_pos\",\n11:     \"left_hip_roll_pos\",\n12:     \"left_hip_yaw_pos\",\n13:     \"left_knee_pos\",\n14:     \"left_ankle_pitch_pos\",\n15:     \"left_ankle_roll_pos\",\n16:     \"right_hip_pitch_pos\",\n17:     \"right_hip_roll_pos\",\n18:     \"right_hip_yaw_pos\",\n19:     \"right_knee_pos\",\n20:     \"right_ankle_pitch_pos\",\n21:     \"right_ankle_roll_pos\",\n22:     \"waist_yaw_pos\",\n23:     \"waist_roll_pos\",\n24:     \"waist_pitch_pos\",\n25:     \"left_shoulder_pitch_pos\",\n26:     \"left_shoulder_roll_pos\",\n27:     \"left_shoulder_yaw_pos\",\n28:     \"left_elbow_pos\",\n29:     \"left_wrist_roll_pos\",\n30:     \"left_wrist_pitch_pos\",\n31:     \"left_wrist_yaw_pos\",\n32:     \"right_shoulder_pitch_pos\",\n33:     \"right_shoulder_roll_pos\",\n34:     \"right_shoulder_yaw_pos\",\n35:     \"right_elbow_pos\",\n36:     \"right_wrist_roll_pos\",\n37:     \"right_wrist_pitch_pos\",\n38:     \"right_wrist_yaw_pos\",\n39:     \"left_hip_pitch_vel\",\n40:     \"left_\n… (23,435 characters left out) …\n:         0.0,\n772:         0.0,\n773:         0.0,\n774:         0.0,\n775:         0.0,\n776:         0.0,\n777:         0.0,\n778:         0.0,\n779:         0.0,\n780:         0.0,\n781:         0.0,\n782:         0.0,\n783:         0.0,\n784:         0.0,\n785:         0.0,\n786:         0.0,\n787:         0.0,\n788:         0.0,\n789:         0.0,\n790:         0.0,\n791:         0.0,\n792:         0.0,\n793:         0.0,\n794:         0.0,\n795:         0.0,\n796:         0.0,\n797:         0.0,\n798:         0.0,\n799:         0.0,\n800:         0.0,\nThe above content does NOT show the entire file contents. If you need to view any lines of the file which were not shown to complete your task, call this tool again to view those lines.\nThe command exited with code 0.\nOutput:\n/tmp/init_obs:\r\ntotal 272\r\ndrwxr-xr-x 4 root root    119 Oct 24 23:19 .\r\ndrwxrwxrwt 1 root root   4096 Oct 24 23:19 ..\r\n-rw-r--r-- 1 root root 259328 Oct 24 23:19 depth_head_left.npy\r\ndrwxr-xr-x 6 root root     86 Oct 24 23:19 frames\r\ndrwxr-xr-x 2 root root    102 Oct 24 23:19 last\r\n-rw-r--r-- 1 root root   5641 Oct 24 23:19 meta.json\r\n-rw-r--r-- 1 root root    700 Oct 24 23:19 proprio.npy\r\n\r\n/tmp/init_obs/last:\r\ntotal 152\r\ndrwxr-xr-x 2 root root   102 Oct 24 23:19 .\r\ndrwxr-xr-x 4 root root   119 Oct 24 23:19 ..\r\n-rw-r--r-- 1 root root 36766 Oct 24 23:19 head_left.jpg\r\n-rw-r--r-- 1 root root 36690 Oct 24 23:19 head_right.jpg\r\n-rw-r--r-- 1 root root 38912 Oct 24 23:19 side.jpg\r\n-rw-r--r-- 1 root root 37159 Oct 24 23:19 venue.jpg","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":5,"t":51.0,"say":"","calls":[{"kind":"shell","label":"reads spec.json","code":"/tmp/spec.json"},{"kind":"shell","label":"reads spec.json","code":"/tmp/spec.json"},{"kind":"look","label":"venue.jpg","code":"/tmp/init_obs/last/venue.jpg"},{"kind":"look","label":"side.jpg","code":"/tmp/init_obs/last/side.jpg"}],"out":{"text":"File Path: `file:///tmp/spec.json`\nTotal Lines: 1702\nTotal Bytes: 38904\nShowing lines 801 to 1600\nThe following code has been modified to include a line number before every line, in the format: <line_number>: <original_line>. Please note that any changes targeting the original code should remove the line number, colon, and leading space.\n801:         0.0,\n802:         0.0,\n803:         0.0,\n804:         0.0,\n805:         0.0,\n806:         0.0,\n807:         0.0\n808:       ],\n809:       \"upper\": [\n810:         2.8798,\n811:         2.9671,\n812:         2.7576,\n813:         2.8798,\n814:         0.5236,\n815:         0.2618,\n816:         2.8798,\n817:         0.5236,\n818:         2.7576,\n819:         2.8798,\n820:         0.5236,\n821:         0.2618,\n822:         2.618,\n823:         0.52,\n824:         0.52,\n825:         2.6704,\n826:         2.2515,\n827:         2.618,\n828:         2.0944,\n829:         1.9722,\n830:         1.6144,\n831:         1.6144,\n832:         2.6704,\n833:         1.5882,\n834:         2.618,\n835:         2.0944,\n836:         1.9722,\n837:         1.6144,\n838:         1.6144,\n839:         32.0,\n840:         20.0,\n841:         32.0,\n842:         20.0,\n843:         37.0,\n844:         37.0,\n845:         32.0,\n846:         20.0,\n847:         32.0,\n848:         20.0,\n849:         37.0,\n850:         37.0,\n851:         32.0,\n852:         37.0,\n853:         37.0,\n854:         37.0,\n855:         37.0,\n856:         37.0,\n857:         37.0,\n858:         37.0,\n859:         22.0\n… (21,065 characters left out) …\n: \"POST /reset abandons the running episode and starts a new one from the same starting state, budget.reset times in all; only the last episode is graded, and a success ends the trial\",\n1691:   \"timing\": {\n1692:     \"realtime\": true,\n1693:     \"slowdown\": 1.0,\n1694:     \"control_dt\": 0.01,\n1695:     \"wall_s_per_control_step\": 0.01,\n1696:     \"clock\": \"an episode starts paused; its clock starts at its first submit or observe with seconds > 0 (also after a reset) and then runs one control step every wall_s_per_control_step seconds, whatever you do, until the episode ends\",\n1697:     \"submit\": \"answers at once: the chunk (rows dt apart, linear or hold) becomes the plan from control step `at` (default: the next one), replacing what was planned from there; rows for steps already gone are dropped (meta.late_steps); after the plan's last row that target is held\",\n1698:     \"observe\": \"seconds = 0: now, at once; seconds > 0: watches the running episode that long and returns what the cameras saw; never changes the plan\",\n1699:     \"max_ahead_steps\": 1000,\n1700:     \"no_images\": \"cameras = [] returns no images, only proprioception (the fastest call)\"\n1701:   }\n1702: }\nThe above content does NOT show the entire file contents. If you need to view any lines of the file which were not shown to complete your task, call this tool again to view those lines.\nThe following is the entire, complete content of the requested file.\nThe following is the entire, complete content of the requested file.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":6,"t":57.0,"say":"","calls":[{"kind":"look","label":"head_left.jpg","code":"/tmp/init_obs/last/head_left.jpg"},{"kind":"shell","label":"Searching /opt and /root for installed files","code":"find /opt /root -maxdepth 4 2>/dev/null"}],"out":{"text":"The following is the entire, complete content of the requested file.\nThe command exited with code 0.\nOutput:\n/opt\r\n/opt/vkclamp\r\n/opt/vkclamp/conf\r\n/opt/vkclamp/conf/vulkan\r\n/opt/vkclamp/conf/vulkan/implicit_layer.d\r\n/opt/vkclamp/libvkclamp.so\r\n/opt/tools\r\n/opt/tools/rcb1.py\r\n/opt/tools/rcb_client.py\r\n/opt/tools/__pycache__\r\n/opt/tools/__pycache__/rcb_client.cpython-312.pyc\r\n/opt/tools/__pycache__/rcb1.cpython-312.pyc\r\n/root\r\n/root/.bashrc\r\n/root/.profile\r\n/root/.local\r\n/root/.local/bin\r\n/root/.local/bin/agy\r\n/root/.gemini\r\n/root/.gemini/antigravity-cli\r\n/root/.gemini/antigravity-cli/antigravity-oauth-token\r\n/root/.gemini/antigravity-cli/settings.json\r\n/root/.gemini/antigravity-cli/log\r\n/root/.gemini/antigravity-cli/log/cli-20261024_231812.log\r\n/root/.gemini/antigravity-cli/log/cli-20261024_231814.log\r\n/root/.gemini/antigravity-cli/cli.log\r\n/root/.gemini/antigravity-cli/crashes\r\n/root/.gemini/antigravity-cli/crashes/crash_234_52becd30-71a1-40a3-b409-1447baf8438c.log\r\n/root/.gemini/antigravity-cli/installation_id\r\n/root/.gemini/antigravity-cli/conversation_summaries.db\r\n/root/.gemini/antigravity-cli/jetski_state.pbtxt\r\n/root/.gemini/antigravity-cli/brain\r\n/root/.gemini/antigravity-cli/brain/a7998060-c046-41f4-bdad-1729128467b0\r\n/root/.gemini/antigravity-cli/conversations\r\n/root/.gemini/antigravity-cli/conversations/a7998060-c046-41f4-bdad-1729128467b0.db\r\n/root/.gemini/antigravity-cli/conversations/a7998060-c046-41f4-bdad-1729128467b0.db-wal\r\n/root/.gemini/antigravity-cli/conv\n… (37 characters left out) …\n9128467b0.db-shm\r\n/root/.gemini/antigravity-cli/hooks.json\r\n/root/.gemini/antigravity-cli/builtin\r\n/root/.gemini/antigravity-cli/builtin/keep.txt\r\n/root/.gemini/antigravity-cli/builtin/skills\r\n/root/.gemini/antigravity-cli/builtin/.checksum\r\n/root/.gemini/antigravity-cli/updater\r\n/root/.gemini/antigravity-cli/updater/update.lock\r\n/root/.gemini/antigravity-cli/updater/update_status.json\r\n/root/.gemini/antigravity-cli/last_check.timestamp\r\n/root/.gemini/antigravity-cli/cache\r\n/root/.gemini/antigravity-cli/cache/default_project_id.txt\r\n/root/.gemini/antigravity-cli/cache/onboarding.json\r\n/root/.gemini/antigravity-cli/cache/last_conversations.json\r\n/root/.gemini/antigravity-cli/implicit\r\n/root/.gemini/antigravity-cli/implicit/df0edfd2-25ae-4c32-99fc-110d4ecb0abe.pb\r\n/root/.gemini/antigravity-cli/presence\r\n/root/.gemini/antigravity-cli/presence/a7998060-c046-41f4-bdad-1729128467b0.lock\r\n/root/.gemini/antigravity-cli/bin\r\n/root/.gemini/antigravity-cli/bin/webm_encoder\r\n/root/.gemini/antigravity-cli/bin/agentapi\r\n/root/.gemini/antigravity-cli/jetbox_summaries_proto.pb\r\n/root/.gemini/antigravity-cli/annotations\r\n/root/.gemini/antigravity-cli/annotations/a7998060-c046-41f4-bdad-1729128467b0.pbtxt\r\n/root/.gemini/config\r\n/root/.gemini/config/hooks.json\r\n/root/.gemini/config/projects\r\n/root/.gemini/config/projects/default-cli-project.json\r\n/root/.gemini/config/mcp_config.json\r\n/root/.gemini/config/.migrated\r\n/root/.gemini/rcb-deny-tool.sh\r\n/root/.gemini/rcb-idle-wake.sh\r\n/root/.wget-hsts","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":7,"t":65.0,"say":"","calls":[{"kind":"shell","label":"Inspecting initial proprioception and depth","code":"python3 -c '\nimport json, numpy as np\nmeta = json.load(open(\"/tmp/init_obs/meta.json\"))\nprint(\"meta keys:\", list(meta.keys()))\nfor k, v in meta.items():\n    if k != \"proprio\":\n        print(f\"  {k}: {v}\")\np = meta[\"proprio\"][\"now\"]\nlayout = meta[\"proprio\"][\"layout\"]\nfor name, val in zip(layout, p):\n    if \"imu\" in name or \"mode\" in name or \"cmd\" in name or \"time\" in name:\n        print(f\"  {name}: {val}\")\ndepth = np.load(\"/tmp/init_obs/depth_head_left.npy\")\nprint(\"depth shape:\", depth.shape, \"min/max:\", depth.min(), depth.max())\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\nmeta keys: ['calls', 'rows', 'control_steps', 'done', 'reason', 'episode', 'control_steps_total', 'resets_left', 'sim_time_s', 'time_s', 'time_left_s', 'proprio', 'running', 'planned_steps', 'lag_s', 'obs_id', 'seconds', 'control_steps_watch', 'ok']\r\n  calls: 1\r\n  rows: 0\r\n  control_steps: 0\r\n  done: False\r\n  reason: None\r\n  episode: 1\r\n  control_steps_total: 0\r\n  resets_left: 5\r\n  sim_time_s: 0.0\r\n  time_s: 0.27\r\n  time_left_s: 7199.7\r\n  running: False\r\n  planned_steps: 0\r\n  lag_s: 0.0\r\n  obs_id: 1\r\n  seconds: 0.0\r\n  control_steps_watch: 0\r\n  ok: True\r\n  imu_pelvis_qw: 1.0\r\n  imu_pelvis_qx: 0.0\r\n  imu_pelvis_qy: 0.0\r\n  imu_pelvis_qz: 0.0\r\n  imu_pelvis_gx: 0.0\r\n  imu_pelvis_gy: 0.0\r\n  imu_pelvis_gz: 0.0\r\n  imu_pelvis_ax: -2.773884671492064e-17\r\n  imu_pelvis_ay: 1.444175160851923e-16\r\n  imu_pelvis_az: -3.801643678571645e-17\r\n  imu_torso_qw: 1.0\r\n  imu_torso_qx: 0.0\r\n  imu_torso_qy: 0.0\r\n  imu_torso_qz: 0.0\r\n  imu_torso_gx: 0.0\r\n  imu_torso_gy: 0.0\r\n  imu_torso_gz: 0.0\r\n  imu_torso_ax: 9.670092085324616e-17\r\n  imu_torso_ay: -4.371911919033053e-17\r\n  imu_torso_az: 7.57242409043608e-18\r\n  mode: 0.0\r\n  cmd_vx: 0.0\r\n  cmd_vy: 0.0\r\n  cmd_wz: 0.0\r\n  time_s: 0.0\r\ndepth shape: (1, 270, 480) min/max: 0.0 9.7","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":8,"t":135.0,"say":"","calls":[{"kind":"shell","label":"Verifying 3D positions of ball machines from depth","code":"python3 -c '\nimport json, numpy as np\ndepth = np.load(\"/tmp/init_obs/depth_head_left.npy\")[0]\nspec = json.load(open(\"/tmp/spec.json\"))\ncam = spec[\"cameras\"][\"head_left\"]\nprint(\"cam:\", cam)\n# Find non-floor points: calculate 3D coordinates of each pixel in robot frame at t=0\nH, W = depth.shape\nfovy = np.deg2rad(cam[\"fovy_deg\"])\nfy = (H / 2.0) / np.tan(fovy / 2.0)\nfx = fy\ncy, cx = H / 2.0, W / 2.0\nu = np.arange(W)[None, :] - cx\nv = np.arange(H)[:, None] - cy\n# Wait: depth note says \"distance along the camera axis; 0 = nothing within max_m\"\nz_cam = depth\nx_cam = (u / fx) * z_cam\ny_cam = -(v / fy) * z_cam # up in image is -v\npos = np.array(cam[\"pose_t0\"][\"position_m\"])\nfwd = np.array(cam[\"pose_t0\"][\"forward\"])\nup = np.array(cam[\"pose_t0\"][\"up\"])\nright = np.array(cam[\"pose_t0\"][\"right\"])\npts = pos[None, None, :] + z_cam[:, :, None] * fwd + x_cam[:, :, None] * right + y_cam[:, :, None] * up\nvalid = (depth > 0.5) & (pts[:, :, 2] > 0.3)\nprint(\"Points with z > 0.3:\", valid.sum())\nfor y_target in [-3.0, -1.5, 0.0, 1.5, 3.0]:\n    mask = valid & (np.abs(pts[:, :, 1] - y_target) < 0.4)\n    if mask.sum() > 0:\n        sub = pts[mask]\n        print(f\"y ~ {y_target}: x in [{sub[:,0].min():.2f}, {sub[:,0].max():.2f}], y_mean={sub[:,1].mean():.2f}, z in [{sub[:,2].min():.2f}, {sub[:,2].max():.2f}]\")\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\ncam: {'rgb': True, 'depth': True, 'hw': [270, 480], 'fovy_deg': 58.0, 'mounted': 'head', 'pose_t0': {'frame': 'the robot at t = 0: origin on the floor under the pelvis, x the way it faces, y left, z up', 'position_m': [0.076, 0.025, 1.227], 'forward': [0.9394, 0.0, -0.3429], 'up': [0.3429, 0.0, 0.9394], 'right': [0.0, -1.0, 0.0]}}\r\nPoints with z > 0.3: 1210\r\ny ~ -3.0: x in [7.66, 8.34], y_mean=-2.93, z in [0.30, 1.73]\r\ny ~ -1.5: x in [7.67, 8.16], y_mean=-1.46, z in [0.30, 1.73]\r\ny ~ 0.0: x in [7.68, 8.15], y_mean=0.02, z in [0.31, 1.73]\r\ny ~ 1.5: x in [7.67, 8.24], y_mean=1.49, z in [0.30, 1.73]\r\ny ~ 3.0: x in [7.67, 8.23], y_mean=2.96, z in [0.30, 1.73]","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":9,"t":146.0,"say":"","calls":[{"kind":"edit","label":"NOTES.md","code":"/app/NOTES.md\n\n# Dodgeball Notes\n- Task: Embodied Olympics dodgeball i00 (standard-realtime mode, S=1, 15.5s episode, 5 throws at 2.0, 4.5, 7.0, 9.5, 12.0s).\n- Approach: Use locomotion policies to move perpendicular to the ball machine line-of-sight after each machine feeds its ball at t_feed, staying well inside court boundaries.\n- Packages installed: none (using standard container numpy, scipy, pillow, requests).\n"},{"kind":"edit","label":"NOTES.md","code":"/app/output/NOTES.md\n\n# Dodgeball Notes\n- Task: Embodied Olympics dodgeball i00 (standard-realtime mode, S=1, 15.5s episode, 5 throws at 2.0, 4.5, 7.0, 9.5, 12.0s).\n- Approach: Use locomotion policies to move perpendicular to the ball machine line-of-sight after each machine feeds its ball at t_feed, staying well inside court boundaries.\n- Packages installed: none (using standard container numpy, scipy, pillow, requests).\n"}],"out":{"text":"Created file file:///app/NOTES.md with requested content.\nIf relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.\nCreated file file:///app/output/NOTES.md with requested content.\nIf relevant, proactively run terminal commands to execute this code for the USER. Don't ask for permission.","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":10,"t":222.0,"say":"","calls":[{"kind":"robot","label":"Benchmarking get_obs latency","code":"python3 -c '\nimport time\nfrom rcb_client import Robot\nr = Robot()\nt0 = time.time()\nfor _ in range(5):\n    r.get_obs(cameras=[], seconds=0)\nt1 = time.time()\nfor _ in range(5):\n    r.get_obs(cameras=[\"venue\"], seconds=0)\nt2 = time.time()\nprint(f\"cameras=[]: {(t1-t0)/5*1000:.1f} ms, cameras=[venue]: {(t2-t1)/5*1000:.1f} ms\")\nprint(\"status:\", r.status())\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\ncameras=[]: 3.0 ms, cameras=[venue]: 6.1 ms\r\nstatus: {'calls': 11, 'rows': 0, 'control_steps': 0, 'done': False, 'reason': None, 'episode': 1, 'control_steps_total': 0, 'resets_left': 5, 'elapsed_s': 275.9, 'running': False, 'planned_steps': 0, 'lag_s': 0.0}","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":11,"t":320.0,"say":"","calls":[{"kind":"shell","label":"Testing 3D-to-2D camera projection math","code":"python3 -c '\nimport json, numpy as np\nfrom PIL import Image\n\nspec = json.load(open(\"/tmp/spec.json\"))\n\ndef project(cam_spec, pts_3d):\n    # pts_3d: (N, 3)\n    H, W = cam_spec[\"hw\"]\n    fovy = np.deg2rad(cam_spec[\"fovy_deg\"])\n    fy = (H / 2.0) / np.tan(fovy / 2.0)\n    fx = fy\n    cx, cy = W / 2.0, H / 2.0\n    pos = np.array(cam_spec[\"pose_t0\"][\"position_m\"])\n    fwd = np.array(cam_spec[\"pose_t0\"][\"forward\"])\n    up = np.array(cam_spec[\"pose_t0\"][\"up\"])\n    right = np.array(cam_spec[\"pose_t0\"][\"right\"])\n    d = pts_3d - pos[None, :]\n    z_c = d @ fwd\n    x_c = d @ right\n    y_c = d @ up\n    u = cx + fx * (x_c / z_c)\n    v = cy - fy * (y_c / z_c)\n    return np.stack([u, v, z_c], axis=-1)\n\nfor cname in [\"venue\", \"side\"]:\n    img = np.array(Image.open(f\"/tmp/init_obs/last/{cname}.jpg\"))\n    pts = np.array([\n        [0.0, 0.0, 0.8],   # robot pelvis at t=0\n        [0.0, 0.0, 0.0],   # robot feet at t=0\n        [0.0, 0.0, 1.3],   # robot head at t=0\n        [8.0, 0.0, 1.2],   # machine 1 exit\n        [-4.0, -4.5, 0.0], # court back-right corner\n        [-4.0, 4.5, 0.0],  # court back-left corner\n        [5.0, 0.0, 0.0],   # centre line middle\n    ])\n    uvz = project(spec[\"cameras\"][cname], pts)\n    print(f\"=== {cname} ===\")\n    for p, (u, v, z) in zip(pts, uvz):\n        ui, vi = int(round(u)), int(round(v))\n        rgb = img[vi, ui].tolist() if (0 <= vi < 270 and 0 <= ui < 480) else None\n        print(f\"  3D {p.tolist()} -> uv=({u:.1f}, {v:.1f}), rgb={rgb}\")\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\n=== venue ===\r\n  3D [0.0, 0.0, 0.8] -> uv=(196.4, 155.2), rgb=[78, 67, 61]\r\n  3D [0.0, 0.0, 0.0] -> uv=(197.4, 171.1), rgb=[176, 145, 116]\r\n  3D [0.0, 0.0, 1.3] -> uv=(195.7, 144.8), rgb=[45, 23, 0]\r\n  3D [8.0, 0.0, 1.2] -> uv=(267.0, 112.1), rgb=[47, 37, 28]\r\n  3D [-4.0, -4.5, 0.0] -> uv=(239.2, 285.1), rgb=None\r\n  3D [-4.0, 4.5, 0.0] -> uv=(48.8, 185.4), rgb=[255, 228, 192]\r\n  3D [5.0, 0.0, 0.0] -> uv=(247.7, 139.6), rgb=[191, 145, 93]\r\n=== side ===\r\n  3D [0.0, 0.0, 0.8] -> uv=(156.0, 141.4), rgb=[56, 37, 31]\r\n  3D [0.0, 0.0, 0.0] -> uv=(157.4, 159.1), rgb=[43, 31, 7]\r\n  3D [0.0, 0.0, 1.3] -> uv=(155.2, 130.0), rgb=[36, 32, 29]\r\n  3D [8.0, 0.0, 1.2] -> uv=(301.1, 123.8), rgb=[84, 69, 74]\r\n  3D [-4.0, -4.5, 0.0] -> uv=(-127.5, 271.8), rgb=None\r\n  3D [-4.0, 4.5, 0.0] -> uv=(79.6, 146.3), rgb=[207, 161, 109]\r\n  3D [5.0, 0.0, 0.0] -> uv=(256.7, 148.2), rgb=[235, 193, 145]","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":12,"t":371.0,"say":"","calls":[{"kind":"shell","label":"Testing background subtraction for robot tracking","code":"python3 -c '\nimport json, numpy as np\nfrom PIL import Image\n\nspec = json.load(open(\"/tmp/spec.json\"))\nvenue_t0 = np.array(Image.open(\"/tmp/init_obs/last/venue.jpg\"), dtype=np.int16)\nside_t0 = np.array(Image.open(\"/tmp/init_obs/last/side.jpg\"), dtype=np.int16)\n\n# In venue_t0, robot is around u in [184, 210], v in [140, 175]\n# In side_t0, robot is around u in [145, 170], v in [125, 165]\n# Let us create clean background images by filling the robot box at t=0 with the wood floor color just to its left/right\nvenue_bg = venue_t0.copy()\nvenue_bg[138:178, 182:212] = np.median(venue_t0[138:178, 165:180], axis=1, keepdims=True)\n\nside_bg = side_t0.copy()\nside_bg[124:166, 144:170] = np.median(side_t0[124:166, 125:140], axis=1, keepdims=True)\n\n# Verify that diff(venue_t0, venue_bg) highlights the robot at (0, 0, 0.8)\ndiff_v = np.abs(venue_t0 - venue_bg).max(axis=-1)\nys, xs = np.where(diff_v > 35)\nprint(\"venue robot pixels:\", len(xs), \"u_mean:\", xs.mean(), \"v_mean:\", ys.mean())\n\ndiff_s = np.abs(side_t0 - side_bg).max(axis=-1)\nys_s, xs_s = np.where(diff_s > 35)\nprint(\"side robot pixels:\", len(xs_s), \"u_mean:\", xs_s.mean(), \"v_mean:\", ys_s.mean())\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\nvenue robot pixels: 231 u_mean: 195.72727272727272 v_mean: 159.64069264069263\r\nside robot pixels: 219 u_mean: 154.51141552511416 v_mean: 146.5799086757991","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":13,"t":636.0,"say":"","calls":[{"kind":"shell","label":"Testing inverse projection from pixel to (x,y)","code":"python3 -c '\nimport json, numpy as np\nspec = json.load(open(\"/tmp/spec.json\"))\n\ndef pixel_to_xy_at_z(cam_spec, u, v, z_plane=0.65):\n    H, W = cam_spec[\"hw\"]\n    fovy = np.deg2rad(cam_spec[\"fovy_deg\"])\n    fy = (H / 2.0) / np.tan(fovy / 2.0)\n    fx = fy\n    cx, cy = W / 2.0, H / 2.0\n    pos = np.array(cam_spec[\"pose_t0\"][\"position_m\"])\n    fwd = np.array(cam_spec[\"pose_t0\"][\"forward\"])\n    up = np.array(cam_spec[\"pose_t0\"][\"up\"])\n    right = np.array(cam_spec[\"pose_t0\"][\"right\"])\n    ray = fwd + ((u - cx) / fx) * right - ((v - cy) / fy) * up\n    t = (z_plane - pos[2]) / ray[2]\n    pt = pos + t * ray\n    return pt[:2]\n\nprint(\"venue -> xy:\", pixel_to_xy_at_z(spec[\"cameras\"][\"venue\"], 195.73, 159.64, 0.60))\nprint(\"side  -> xy:\", pixel_to_xy_at_z(spec[\"cameras\"][\"side\"], 154.51, 146.58, 0.60))\n'"}],"out":{"text":"The command exited with code 0.\nOutput:\nvenue -> xy: [-0.05518764  0.00766164]\r\nside  -> xy: [-0.0757481  -0.12307396]","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}},{"id":14,"t":673.0,"say":"This request was blocked by Gemini's filters. They can occasionally trigger by mistake on safe coding, security, or biology-related queries. Please try rephrasing your prompt. You can [send feedback](https://ai.google.dev/gemini-api/docs/troubleshooting#file-bug) or read more about [our policies here](https://policies.google.com/terms/generative-ai/use-policy).","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":null,"cached":null,"out":null,"think":null,"usd":0},"sim":{"submit":0,"reset":0}}]}

runs/embodied-olympics-fun-dunk-i00-standard--ngifgj5.json

@@ -0,0 +1 @@
+{"slug":"embodied-olympics-fun-dunk-i00-standard--ngifgj5","task":"embodied-olympics-fun-dunk-i00-standard","bench":"embodied-olympics-fun","mode":"standard","run":"codex-gpt6_luna-xhigh","agent":"codex","model":"gpt-6-luna","effort":"xhigh","state":"failed","success":0,"reward":{"success":0,"live_success":0,"graded_by_live":1,"replays":0,"live_rule":1,"live_verdict_error":0,"finalized":1,"missing_log":0,"infra_error":0,"grader_error":0,"sim_error":0,"sim_crashed_on_call":0,"instance_mismatch":0,"n_actions":62,"sim_steps":310,"calls":24,"rows":310,"resets":5,"reset_budget":5,"sim_startups":1,"elapsed_s":402.8,"video_rendered":1,"first_success":0,"first_reason_code":2,"resource_enforced":0,"limits_violation_observed":0,"resource_monitor_missing":0,"peak_gpu_memory_gb":0,"peak_memory_gb":0.312,"peak_processes":8,"peak_disk_gb":0.833,"resource_emergency_kills":0,"resource_oom_kills":0,"resource_host_oom_kills":0,"resource_service_oom_kills":0},"wall_s":450,"tokens":{"in":2414810,"cached":2317586,"out":55064,"usd":0.06},"task_text":"# embodied-olympics dunk — instance i00 — STANDARD MODE (robot as a service)\n\nYou control a simulated Unitree H1-2 humanoid robot (27 joints, 1.78 m tall, 67 kg; the Embodied Olympics simulator on MuJoCo)\n**as if it were real hardware**: the simulator runs in a service you cannot open, and you reach it only through a\nclient. **Task:** Dunk the basketball with both hands: holding it in your two hands, jump, force it down through the ring from above with both hands on it, and land on your feet.\n\n## The venue\nA FIBA court. You stand under the basket's front, facing it (+x), inside the no-charge semi-circle: the point under the ring's centre is 0.75 m ahead of your pelvis, the backboard's face 0.375 m beyond it. The ring's top edge is 2.39 m above the floor. The ball is held between your two hands in front of your chest, its centre 1.13 m above the floor and 0.39 m ahead of your pelvis: the hands open (grip values 0, the fingers straight), the palms on its two sides (on these hands it bears on the two thumbs, which lie a little proud of the palms), and the arms pressing the palms in (the start command's shoulder roll and yaw are a little further in than the arms stand: the difference is the squeeze). A grip value is that hand's closure command (0 open, 1 every finger and thumb joint closing towards its closed angle; a finger stops on what it touches).\n\nDirections are from the robot at the start: +x is the way you face, +y is to your left, +z is up; the floor is level.\n\n\n## The rules\n**Rule book:** FIBA Official Basketball Rules 2024: Art. 2 (court), Art. 3 and the Basketball Equipment appendix (backboard, ring, size-7 ball), Art. 14 (control: a player holds a live ball), Art. 15 (a dunk: the ball forced downwards into the basket with one or both hands; here both), Art. 16 (goal; a ball that passes through the basket from below is a violation), Art. 25 (travelling), Art. 36 (technical foul: hanging on the ring); FIBA Mini-Basketball Rules (the ring at 2.60 m). Where this task departs from it:\n\n- Basketball Equipment (basket): the basket is lowered: the ring's top edge is 2.39 m above the floor, below FIBA Mini-Basketball's 2.60 m (FIBA's own ring: 3.05 m), set at the robot's measured reach (H1-2 with H2-class leg actuators): from this start, the ball squeezed between the open hands, a searched trajectory on the robot's own motors dunks and lands: a deep squat, a jump with an arm swing (the pelvis 0.43 m above its standing height), the fingers half closed over the ball in the air, the ball's centre at most 2.53 m, carried down into the ring by both hands about 0.10 m from its axis and through it 2.74 s into the episode, then a landing on the walking policy, standing at 4.8 s; the energy is paid for by the actuators' work (in the take-off +671 J of actuator work for +605 J of mechanical energy). The ring is at the edge of the robot's reach: searched open-loop jumps with the ball raised from the chest brought it no nearer than 0.24 m to the ring's axis w\n… (18,606 characters left out) …\nng between the pads in m, its speed in m/s, + opening, and the clamp force in N, + closing); a gripper's `holding_*` is 1 while both its jaws touch the same object. A\nreal H1-2 has no sensor for its position in the venue, and neither do you: that is what the cameras are for.\n\n## External material\nThe network is open. You may install packages (pip, apt), and you may search for, download and use public tools, robot models and pretrained controllers (for example a published Unitree G1 walking, jumping or whole-body policy): finding existing work and adapting it to this robot is encouraged. Say in your notes what you used. You may read anything installed in your container, this benchmark's own source code included. The one thing not to look up is a ready-made solution to this exact task, such as this benchmark's reference solutions. This is an honour rule; trials are reviewed.\n\n## Resource limits\nLimits for your agent environment, your own container only (the simulator in the `sim` service is the benchmark's and does not count): at most **8 GiB of memory**, **512 processes** and **20 GiB** of new files under `/app`, `/tmp` and `/root`; your container has no GPU. These limits may be enforced (from protocol v1.2 on): when enforcement is on, exceeding one for 60 s within any 2 minutes, or a process killed for lack of memory, ends the trial as a failure (reward 0), whatever the deliverable. A monitor outside your container measures every few seconds; the current values are in `/run/rcb-limits/usage.json`. CPU is capped at 4 cores for your container; the cap only slows you down.\n\n## Time limit\nYou have 7200 seconds to complete this task. Do not cheat by using online solutions or hints specific to this task.\n\nThis is wall-clock time from the start of your session; the time you wait for the simulator service counts. When it is up you are stopped without warning and the episode is graded as it stands at that moment. The service's `time_left_s` counts the same limit from your first request to the service, so it never runs out before your time does: your real time left is `time_left_s` minus the time you spent before that request. Plan by it. Your session started when you received this task. `/run/rcb-limits/usage.json`, rewritten every few seconds, gives the time left on this limit (`time_left_s`) and its end (`deadline_utc`), counted from a little before your session started, so it never gives you more time than you have: read it (or run `date`) before you decide that time is short, never guess. Waiting counts too: a tool call that sends input to a process that keeps running (an interactive interpreter, a simulator loop, a server) can wait for the whole wait or yield time you give it (for example Codex's `yield_time_ms`) even when the answer is printed at once, so give such calls a short wait and poll again if the output is not complete, or run one-shot scripts that exit when they are done. Installing packages is not cheating (`## External material` above).\n","step_times":"recorded","sim_total":{"submit":17,"reset":5},"replay":"embodied-olympics-fun-dunk-i00-standard--ngifgj5/replay.mp4","replay_last":"embodied-olympics-fun-dunk-i00-standard--ngifgj5/replay_last.jpg","steps":[{"id":6,"t":0.0,"say":"I’ll inspect the client protocol and robot spec, save a small control script and notes early, then use the available attempts to tune the motion from proprioception and camera feedback.","calls":[{"kind":"shell","label":"pwd && ls -la && cat /run/rcb-limits/usage.json && python -c \"import rcb_client; help(rcb_client)\"","code":"pwd && ls -la && cat /run/rcb-limits/usage.json && python -c \"import rcb_client; help(rcb_client)\""}],"out":{"text":"/app\ntotal 12\ndrwxr-xr-x 3 root root 4096 Oct 19 06:15 .\ndrwxr-xr-x 1 root root 4096 Oct 25 01:14 ..\ndrwxr-xr-x 2 root root 4096 Oct 19 06:15 output\n{\n \"t\": 1792890847.91,\n \"measured\": true,\n \"gpu_memory_gb\": 0.0,\n \"memory_gb\": 0.312,\n \"processes\": 4,\n \"disk_gb\": 0.61,\n \"enforced\": 0,\n \"limits\": {\n  \"max_gpu_memory_gb\": 0.0,\n  \"max_memory_gb\": 8.0,\n  \"max_processes\": 512,\n  \"max_disk_gb\": 20.0,\n  \"sustain_sec\": 60.0,\n  \"window_sec\": 120.0\n },\n \"violations\": [],\n \"emergency_kills\": 0,\n \"last_emergency_kill\": null,\n \"elapsed_s\": 130.0,\n \"time_limit_s\": 7200,\n \"time_left_s\": 7070.0,\n \"deadline_utc\": \"2026-10-25T03:11:57Z\"\n}Help on module rcb_client:\n\nNAME\n    rcb_client - eai-standard/2.1 client: your whole interface to the robot (installed at /opt/tools/rcb_client.py, on PYTHONPATH).\n\nDESCRIPTION\n        from rcb_client import Robot, EpisodeOver\n        r = Robot()            # finds the service (SIM_URL, http://sim:8000, http://localhost:8000), waits until it is ready\n\n    Calls (each is one HTTP request; every answer that carries data is an RCB1 frame, decoded into a Result):\n        r.spec()                                 GET /spec, fetched when connecting: the robot's facts. \"action_types\"\n                                                 (\"joint_position\": dim, layout (actuator order), lower/upper, range_rad,\n                                                 units, semantics), \"control_dt\" (0.02 s), \"max_control_steps\" (the\n                                                 e\n… (6,492 characters left out) …\n|  ----------------------------------------------------------------------\n     |  Data descriptors defined here:\n     |\n     |  __dict__\n     |      dictionary for instance variables\n     |\n     |  __weakref__\n     |      list of weak references to the object\n\n    class Robot(builtins.object)\n     |  Robot(base: 'str | None' = None, timeout: 'float' = 600.0)\n     |\n     |  Methods defined here:\n     |\n     |  __init__(self, base: 'str | None' = None, timeout: 'float' = 600.0)\n     |      Initialize self.  See help(type(self)) for accurate signature.\n     |\n     |  get_obs(self, cameras=None, seconds: 'float' = 0.0, video_fps: 'int' = 10, include_depth: 'bool' = False, video: 'str' = 'frames') -> 'Result'\n     |\n     |  reset(self, cameras=None, include_depth: 'bool' = False) -> 'Result'\n     |\n     |  spec(self) -> 'dict'\n     |\n     |  status(self) -> 'dict'\n     |\n     |  submit(self, action_type: 'str', actions, dt: 'float | None' = None, interpolate: 'str' = 'linear', cameras=None, video_fps: 'int' = 10, video: 'str' = 'mp4', include_depth: 'bool' = False, note: 'str' = '') -> 'Result'\n     |\n     |  ----------------------------------------------------------------------\n     |  Data descriptors defined here:\n     |\n     |  __dict__\n     |      dictionary for instance variables\n     |\n     |  __weakref__\n     |      list of weak references to the object\n\nDATA\n    MAX_BODY = 67108864\n    PROTOCOLS = ('eai-standard/2.', 'rcb-limited/2.')\n\nFILE\n    /opt/tools/rcb_client.py\n\n\n","exit":[],"pics":[]},"tok":{"in":16348,"cached":null,"out":171,"think":55,"usd":0.0021},"sim":{"submit":0,"reset":0}},{"id":7,"t":4.4,"say":"","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":18852,"cached":16345,"out":609,"think":175,"usd":0.0008},"sim":{"submit":0,"reset":0}},{"id":8,"t":5.8,"say":"","calls":[{"kind":"shell","label":"python /app/output/gen.py","code":"python /app/output/gen.py"}],"out":{"text":"connected {'calls': 0, 'rows': 0, 'control_steps': 0, 'done': False, 'reason': None, 'episode': 1, 'control_steps_total': 0, 'resets_left': 5, 'elapsed_s': 0.0}\ninitial {\"calls\": 1, \"rows\": 0, \"control_steps\": 0, \"done\": false, \"reason\": null, \"episode\": 1, \"control_steps_total\": 0, \"resets_left\": 5, \"sim_time_s\": 0.0, \"time_s\": 1.35, \"time_left_s\": 7198.7, \"proprio\": {\"layout\": [\"left_hip_yaw_pos\", \"left_hip_pitch_pos\", \"left_hip_roll_pos\", \"left_knee_pos\", \"left_ankle_pitch_pos\", \"left_ankle_roll_pos\", \"right_hip_yaw_pos\", \"right_hip_pitch_pos\", \"right_hip_roll_pos\", \"right_knee_pos\", \"right_ankle_pitch_pos\", \"right_ankle_roll_pos\", \"torso_pos\", \"left_shoulder_pitch_pos\", \"left_shoulder_roll_pos\", \"left_shoulder_yaw_pos\", \"left_elbow_pitch_pos\", \"left_elbow_roll_pos\", \"left_wrist_pitch_pos\", \"left_wrist_yaw_pos\", \"right_shoulder_pitch_pos\", \"right_shoulder_roll_pos\", \"right_shoulder_yaw_pos\", \"right_elbow_pitch_pos\", \"right_elbow_roll_pos\", \"right_wrist_pitch_pos\", \"right_wrist_yaw_pos\", \"left_hip_yaw_vel\", \"left_hip_pitch_vel\", \"left_hip_roll_vel\", \"left_knee_vel\", \"left_ankle_pitch_vel\", \"left_ankle_roll_vel\", \"right_hip_yaw_vel\", \"right_hip_pitch_vel\", \"right_hip_roll_vel\", \"right_knee_vel\", \"right_ankle_pitch_vel\", \"right_ankle_roll_vel\", \"torso_vel\", \"left_shoulder_pitch_vel\", \"left_shoulder_roll_vel\", \"left_shoulder_yaw_vel\", \"left_elbow_pitch_vel\", \"left_elbow_roll_vel\", \"left_wrist_pitch_vel\", \"left_wrist_yaw_vel\", \"right_shoulder_pitch_vel\", \"right_shoulder_roll_ve\n… (2,863 characters left out) …\n.20000000298023224, 0.0, 0.0, -0.38670000433921814, 0.3303000032901764, -1.076799988746643, 0.5031999945640564, 0.10419999808073044, -0.46380001306533813, 1.2791999578475952, -0.38670000433921814, -0.3303000032901764, 1.076799988746643, 0.5031999945640564, -0.10419999808073044, -0.46380001306533813, -1.2791999578475952, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1599999964237213, 0.0, 0.36000001430511475, -0.20000000298023224, 0.0, 0.0, -0.1599999964237213, 0.0, 0.36000001430511475, -0.20000000298023224, 0.0, 0.0, -0.4300000071525574, 0.25290000438690186, -1.1318000555038452, 0.4546000063419342, 0.20739999413490295, -0.4399999976158142, 1.2400000095367432, -0.4300999939441681, -0.25290000438690186, 1.132099986076355, 0.4546000063419342, -0.20730000734329224, -0.4399999976158142, -1.2400000095367432, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.0915592759847641, -0.03659094497561455, 0.013778342865407467, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.13652819395065308, 0.029058992862701416, 0.01103812176734209, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}, \"obs_id\": 1, \"seconds\": 0.0, \"control_steps_watch\": 0, \"ok\": true}\n","exit":[],"pics":[]},"tok":{"in":19485,"cached":18849,"out":100,"think":53,"usd":0.0003},"sim":{"submit":0,"reset":0}},{"id":9,"t":9.3,"say":"","calls":[{"kind":"shell","label":"s=json.load(open('/app/output/spec.json'))","code":"python - <<'PY'\nimport json\ns=json.load(open('/app/output/spec.json'))\nprint('TOP',s.keys())\nfor k in ['action_types','control_dt','max_control_steps','max_chunk_s','cameras','robot_facts','start_command','budget','rules','episode']:\n v=s.get(k)\n if k in ('robot_facts','rules'): print(k, json.dumps(v,indent=2)[:5000])\n else: print(k,json.dumps(v,indent=2)[:8000])\nPY\nfind /app/output/initial_obs -maxdepth 2 -type f"}],"out":{"text":"TOP dict_keys(['protocol', 'control_dt', 'max_control_steps', 'max_chunk_s', 'max_watch_s', 'max_video_fps', 'max_frames_per_call', 'proprio_layout', 'budget', 'clock', 'on_service_error', 'simulator', 'robot', 'task_text', 'rules', 'episode', 'action_types', 'start_command', 'units', 'cameras', 'depth', 'robot_facts', 'proprio', 'referee', 'reset'])\naction_types {\n  \"stand\": {\n    \"dim\": 17,\n    \"layout\": [\n      \"torso\",\n      \"left_shoulder_pitch\",\n      \"left_shoulder_roll\",\n      \"left_shoulder_yaw\",\n      \"left_elbow_pitch\",\n      \"left_elbow_roll\",\n      \"left_wrist_pitch\",\n      \"left_wrist_yaw\",\n      \"right_shoulder_pitch\",\n      \"right_shoulder_roll\",\n      \"right_shoulder_yaw\",\n      \"right_elbow_pitch\",\n      \"right_elbow_roll\",\n      \"right_wrist_pitch\",\n      \"right_wrist_yaw\",\n      \"grip_left\",\n      \"grip_right\"\n    ],\n    \"lower\": [\n      -2.35,\n      -3.14,\n      -0.38,\n      -2.66,\n      -0.95,\n      -3.01,\n      -0.47,\n      -1.27,\n      -3.14,\n      -3.4,\n      -3.01,\n      -0.95,\n      -2.75,\n      -0.47,\n      -1.27,\n      0.0,\n      0.0\n    ],\n    \"upper\": [\n      2.35,\n      1.57,\n      3.4,\n      3.01,\n      3.18,\n      2.75,\n      0.47,\n      1.27,\n      1.57,\n      0.38,\n      2.66,\n      3.18,\n      3.01,\n      0.47,\n      1.27,\n      1.0,\n      1.0\n    ],\n    \"meaning\": \"the platform holds the legs in the stance; you give the waist's 1 and the arms' 14 joint targets (rad) (grip_left: the left hand's closure command, 0 (open, the fingers straigh\n… (15,352 characters left out) …\ne foot may not touch down twice in a row; a foot on the floor may not slide more than 0.2 m from where it stood or was put down; after that no foot may touch the floor again until the ball is through the ring. Hanging on the ring: you may touch the basket (the ring, its plate, the backboard) with any part of you for at most 0.3 s in all. After the dunk let go of the ball (no hand on it) and stand: for 1.0 s without a break only your feet touch the ground, your torso is upright (the cosine of its tilt at least 0.94) and your pelvis moves slower than 0.35 m/s. Then you have succeeded. You fail if the ball leaves your hands before its centre has entered the ring; if the ball touches the floor before the dunk; if the ball goes through the ring after an entry at which not both hands were on it (a one-hand dunk, a tip, a shot or a drop); if, let go inside the ring, it touches the floor without having gone through; if the ball comes up through the ring from below; if you travel; if you hang on the ring; if you fall at any time (a fall: your pelvis lower than 0.35 m above the floor, or any part of you other than your feet on the floor); or after 15 s.\"\nepisode \"one attempt: it ends at the success, at a rule violation (a fall, a foul; the rules say which), or after 15 s (1500 control steps)\"\n/app/output/initial_obs/proprio.npy\n/app/output/initial_obs/meta.json\n/app/output/initial_obs/last/head_left.jpg\n/app/output/initial_obs/last/venue.jpg\n/app/output/initial_obs/depth_head_left.npy\n","exit":[],"pics":[]},"tok":{"in":22034,"cached":19482,"out":198,"think":35,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":10,"t":10.4,"say":"","calls":[{"kind":"look","label":"venue.jpg","code":"/app/output/initial_obs/last/venue.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s010_0.jpg"]},"tok":{"in":28787,"cached":22031,"out":48,"think":8,"usd":0.0011},"sim":{"submit":0,"reset":0}},{"id":11,"t":12.1,"say":"","calls":[{"kind":"look","label":"head_left.jpg","code":"/app/output/initial_obs/last/head_left.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s011_0.jpg"]},"tok":{"in":29021,"cached":28784,"out":56,"think":16,"usd":0.0003},"sim":{"submit":0,"reset":0}},{"id":12,"t":22.8,"say":"The service is ready and the first attempt is untouched. The spec confirms 10 ms `joints` control and the robot’s start pose; I’ve saved the controller and notes. I’m mapping arm orientation and grasp contact before spending an attempt on the jump.","calls":[{"kind":"shell","label":"s=json.load(open('/app/output/spec.json')); p=s['proprio_layout']; x=json.load(open('/app/output/initial_obs/m","code":"python - <<'PY'\nimport json,numpy as np\ns=json.load(open('/app/output/spec.json')); p=s['proprio_layout']; x=json.load(open('/app/output/initial_obs/meta.json'))['proprio']['now']\nfor n,v in zip(p,x):\n if ('hand_' in n or n.startswith('holding') or n.startswith('imu_pelvis_') or n.startswith('imu_torso_')): print(n,round(v,4))\nprint('LINKS',json.dumps(s['robot_facts'].get('links'),indent=2)[:3000])\nPY\nrg --files /opt | rg -i '(h1.*urdf|unitree.*urdf|h1.*mjcf|h1.*xml)$' | head -30"}],"out":{"text":"imu_pelvis_qw 1.0\nimu_pelvis_qx 0.0\nimu_pelvis_qy 0.0\nimu_pelvis_qz 0.0\nimu_pelvis_gx 0.0\nimu_pelvis_gy 0.0\nimu_pelvis_gz 0.0\nimu_pelvis_ax -0.0916\nimu_pelvis_ay -0.0366\nimu_pelvis_az 0.0138\nimu_torso_qw 1.0\nimu_torso_qx 0.0\nimu_torso_qy 0.0\nimu_torso_qz 0.0\nimu_torso_gx 0.0\nimu_torso_gy 0.0\nimu_torso_gz 0.0\nimu_torso_ax -0.1365\nimu_torso_ay 0.0291\nimu_torso_az 0.011\nholding_left 0.0\nholding_right 0.0\nhand_closure_left 0.0\nhand_closure_speed_left 0.0\nhand_force_left 0.0\nleft_hand_thumb_yaw_pos 0.0\nleft_hand_thumb_pitch_pos 0.0\nleft_hand_index_pos 0.0\nleft_hand_middle_pos 0.0\nleft_hand_ring_pos 0.0\nleft_hand_pinky_pos 0.0\nleft_hand_thumb_yaw_torque 0.0\nleft_hand_thumb_pitch_torque 0.0\nleft_hand_index_torque 0.0\nleft_hand_middle_torque 0.0\nleft_hand_ring_torque 0.0\nleft_hand_pinky_torque 0.0\nhand_closure_right 0.0\nhand_closure_speed_right 0.0\nhand_force_right 0.0\nright_hand_thumb_yaw_pos 0.0\nright_hand_thumb_pitch_pos 0.0\nright_hand_index_pos 0.0\nright_hand_middle_pos 0.0\nright_hand_ring_pos 0.0\nright_hand_pinky_pos 0.0\nright_hand_thumb_yaw_torque 0.0\nright_hand_thumb_pitch_torque 0.0\nright_hand_index_torque 0.0\nright_hand_middle_torque 0.0\nright_hand_ring_torque 0.0\nright_hand_pinky_torque 0.0\nLINKS null\n","exit":[],"pics":[]},"tok":{"in":29263,"cached":29018,"out":1702,"think":1430,"usd":0.0012},"sim":{"submit":0,"reset":0}},{"id":13,"t":26.0,"say":"","calls":[{"kind":"shell","label":"print('robot facts links keys', s['robot_facts'].keys())","code":"ls /opt; python - <<'PY'\nfrom rcb_client import Robot\nr=Robot(); s=r.spec()\nprint('robot facts links keys', s['robot_facts'].keys())\nprint('proprio details',s['proprio'])\nprint('episode',s['episode'])\nprint('referee',json.dumps(s.get('referee'),indent=2)[:3000])\nPY"}],"out":{"text":"tools\nTraceback (most recent call last):\n  File \"<stdin>\", line 6, in <module>\nNameError: name 'json' is not defined. Did you forget to import 'json'?\nrobot facts links keys dict_keys(['name', 'title', 'revision', 'model', 'joints', 'n_legs', 'n_waist', 'n_arms', 'range_rad', 'speed_limit_rad_s', 'torque_limit_nm', 'default_kp', 'default_kd', 'kp_max', 'kd_max', 'stance_legs_rad', 'rest_upper_rad', 'mass_kg', 'height_m', 'pelvis_height_standing_m', 'control_dt_s', 'physics_dt_s', 'walk_command_max', 'run_command_range', 'real_hands'])\nproprio details {'units': \"*_pos, *_target: rad; *_vel: rad/s; *_torque: N m (the motor torque applied); imu_*: orientation quaternion (w, x, y, z; world from sensor), angular velocity (rad/s) and acceleration (m/s^2, gravity included) in the sensor's frame; holding_*: 1 while that hand grips something; mode: 0 stand, 1 walk, 2 run, 3 run_arms, 4 joints / lowcmd, 5 walk_falcon, 6 walk_agile (the G1's); cmd_*: the velocity command in force; time_s: the episode's time; jaw_opening_* (m), jaw_speed_* (m/s, + opening), jaw_force_* (N, the clamp force, + closing): a gripper's jaws; its holding_* is 1 while both jaws touch the same object\"}\nepisode one attempt: it ends at the success, at a rule violation (a fall, a foul; the rules say which), or after 15 s (1500 control steps)\n","exit":[],"pics":[]},"tok":{"in":31517,"cached":29260,"out":254,"think":122,"usd":0.0007},"sim":{"submit":0,"reset":0}},{"id":14,"t":29.7,"say":"","calls":[{"kind":"robot","label":"a=np.tile(row,(30,1)); z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport json, numpy as np\nfrom rcb_client import Robot\nr=Robot(); row=r.spec()['start_command']['row'].copy(); row[-2:]=[0.4,0.4]\na=np.tile(row,(30,1)); z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none')\nprint('meta', {k:z.meta.get(k) for k in ['done','reason','time_s','control_steps','resets_left']})\nlay=z.meta['proprio']['layout']; x=z.meta['proprio']['now']; print({n:round(x[lay.index(n)],3) for n in ['holding_left','holding_right','hand_closure_left','hand_closure_right','hand_force_left','hand_force_right','left_hand_index_pos','right_hand_index_pos','time_s']})\nz.save('/app/output/grip_test')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":32195,"cached":31514,"out":429,"think":183,"usd":0.0006},"sim":{"submit":1,"reset":0}},{"id":15,"t":32.1,"say":"","calls":[{"kind":"look","label":"venue.jpg","code":"/app/output/grip_test/last/venue.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s015_0.jpg"]},"tok":{"in":32773,"cached":32192,"out":80,"think":40,"usd":0.0004},"sim":{"submit":0,"reset":0}},{"id":16,"t":34.0,"say":"","calls":[{"kind":"robot","label":"z=Robot().get_obs(cameras=['head_left'],video='none',include_depth=True); z.save('/app/output/head_after_grip'","code":"python - <<'PY'\nfrom rcb_client import Robot\nz=Robot().get_obs(cameras=['head_left'],video='none',include_depth=True); z.save('/app/output/head_after_grip')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":33039,"cached":32770,"out":177,"think":90,"usd":0.0004},"sim":{"submit":0,"reset":0}},{"id":17,"t":35.8,"say":"","calls":[{"kind":"look","label":"head_left.jpg","code":"/app/output/head_after_grip/last/head_left.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s017_0.jpg"]},"tok":{"in":33239,"cached":33036,"out":39,"think":0,"usd":0.0004},"sim":{"submit":0,"reset":0}},{"id":18,"t":48.2,"say":"","calls":[{"kind":"shell","label":"urls=['https://raw.githubusercontent.com/unitreerobotics/unitree_ros/master/robots/h1_description/urdf/h1.urdf","code":"python - <<'PY'\nimport urllib.request\nurls=['https://raw.githubusercontent.com/unitreerobotics/unitree_ros/master/robots/h1_description/urdf/h1.urdf','https://raw.githubusercontent.com/unitreerobotics/unitree_ros2/master/robots/h1_description/urdf/h1.urdf']\nfor u in urls:\n try:\n  d=urllib.request.urlopen(u,timeout=8).read()\n  open('/app/output/unitree_h1.urdf','wb').write(d)\n  print('downloaded',u,len(d)); break\n except Exception as e: print(type(e).__name__,e)\nPY\nrg -n 'joint name=\"(left|right)_(shoulder|hip|knee|ankle)' /app/output/unitree_h1.urdf | head -60"}],"out":{"text":"downloaded https://raw.githubusercontent.com/unitreerobotics/unitree_ros/master/robots/h1_description/urdf/h1.urdf 23317\n60:  <joint name=\"left_hip_yaw_joint\" type=\"revolute\">\r\n89:  <joint name=\"left_hip_roll_joint\" type=\"revolute\">\r\n118:  <joint name=\"left_hip_pitch_joint\" type=\"revolute\">\r\n147:  <joint name=\"left_knee_joint\" type=\"revolute\">\r\n176:  <joint name=\"left_ankle_joint\" type=\"revolute\">\r\n205:  <joint name=\"right_hip_yaw_joint\" type=\"revolute\">\r\n234:  <joint name=\"right_hip_roll_joint\" type=\"revolute\">\r\n263:  <joint name=\"right_hip_pitch_joint\" type=\"revolute\">\r\n292:  <joint name=\"right_knee_joint\" type=\"revolute\">\r\n321:  <joint name=\"right_ankle_joint\" type=\"revolute\">\r\n379:  <joint name=\"left_shoulder_pitch_joint\" type=\"revolute\">\r\n408:  <joint name=\"left_shoulder_roll_joint\" type=\"revolute\">\r\n437:  <joint name=\"left_shoulder_yaw_joint\" type=\"revolute\">\r\n495:  <joint name=\"right_shoulder_pitch_joint\" type=\"revolute\">\r\n524:  <joint name=\"right_shoulder_roll_joint\" type=\"revolute\">\r\n553:  <joint name=\"right_shoulder_yaw_joint\" type=\"revolute\">\r\n","exit":[],"pics":[]},"tok":{"in":33464,"cached":33236,"out":1960,"think":1738,"usd":0.0013},"sim":{"submit":0,"reset":0}},{"id":19,"t":50.4,"say":"","calls":[{"kind":"shell","label":"sed -n '110,205p' /app/output/unitree_h1.urdf; sed -n '370,495p' /app/output/unitree_h1.urdf; sed -n '495,610p","code":"sed -n '110,205p' /app/output/unitree_h1.urdf; sed -n '370,495p' /app/output/unitree_h1.urdf; sed -n '495,610p' /app/output/unitree_h1.urdf"}],"out":{"text":"    </visual>\r\n    <collision>\r\n      <origin xyz=\"0 0 -0.2\" rpy=\"0 0 0\"/>\r\n      <geometry>\r\n        <cylinder radius=\"0.05\" length=\"0.2\"/>\r\n      </geometry>\r\n    </collision>\r\n  </link>\r\n  <joint name=\"left_hip_pitch_joint\" type=\"revolute\">\r\n    <origin xyz=\"0 0.11536 0\" rpy=\"0 0 0\"/>\r\n    <parent link=\"left_hip_roll_link\"/>\r\n    <child link=\"left_hip_pitch_link\"/>\r\n    <axis xyz=\"0 1 0\"/>\r\n    <limit lower=\"-3.14\" upper=\"2.53\" effort=\"200\" velocity=\"23\"/>\r\n  </joint>\r\n  <link name=\"left_knee_link\">\r\n    <inertial>\r\n      <origin xyz=\"0.003406 -0.005279 -0.143702\" rpy=\"0 0 0\"/>\r\n      <mass value=\"2.824\"/>\r\n      <inertia ixx=\"23792.186E-06\" ixy=\"-201.965E-06\" ixz=\"1348.223E-06\" iyy=\"24505.066E-06\" iyz=\"342.730E-06\" izz=\"3235.744E-06\"/>\r\n    </inertial>\r\n    <visual>\r\n      <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\r\n      <geometry>\r\n        <mesh filename=\"package://h1_description/meshes/left_knee_link.STL\"/>\r\n      </geometry>\r\n      <material name=\"\">\r\n        <color rgba=\"0.1 0.1 0.1 1\"/>\r\n      </material>\r\n    </visual>\r\n    <collision>\r\n      <origin xyz=\"0 0 -0.2\" rpy=\"0 0 0\"/>\r\n      <geometry>\r\n        <cylinder radius=\"0.05\" length=\"0.2\"/>\r\n      </geometry>\r\n    </collision>\r\n  </link>\r\n  <joint name=\"left_knee_joint\" type=\"revolute\">\r\n    <origin xyz=\"0 0 -0.4\" rpy=\"0 0 0\"/>\r\n    <parent link=\"left_hip_pitch_link\"/>\r\n    <child link=\"left_knee_link\"/>\r\n    <axis xyz=\"0 1 0\"/>\r\n    <limit lower=\"-0.26\" upper=\"2.05\" effort=\"300\" velocity=\"14\"/>\r\n  </joint>\r\n  <link name\n… (8,854 characters left out) …\n88\" ixy=\"3.6086E-05\" ixz=\"0.00029293\" iyy=\"0.0060062\" iyz=\"-4.664E-06\" izz=\"0.0060023\"/>\r\n    </inertial>\r\n    <visual>\r\n      <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\r\n      <geometry>\r\n        <mesh filename=\"package://h1_description/meshes/right_elbow_link.STL\"/>\r\n      </geometry>\r\n      <material name=\"\">\r\n        <color rgba=\"0.1 0.1 0.1 1\"/>\r\n      </material>\r\n    </visual>\r\n    <collision>\r\n      <origin xyz=\"0.125 0 0\" rpy=\"0 1.57 0\"/>\r\n      <geometry>\r\n        <cylinder radius=\"0.03\" length=\"0.09\"/>\r\n      </geometry>\r\n    </collision>\r\n  </link>\r\n  <joint name=\"right_elbow_joint\" type=\"revolute\">\r\n    <origin xyz=\"0.0185 0 -0.198\" rpy=\"0 0 0\"/>\r\n    <parent link=\"right_shoulder_yaw_link\"/>\r\n    <child link=\"right_elbow_link\"/>\r\n    <axis xyz=\"0 1 0\"/>\r\n    <limit lower=\"-1.25\" upper=\"2.61\" effort=\"18\" velocity=\"20\"/>\r\n  </joint>\r\n  <link name=\"imu_link\"></link>\r\n  <joint name=\"imu_joint\" type=\"fixed\">\r\n    <origin xyz=\"-0.04452 -0.01891 0.27756\" rpy=\"0 0 0\"/>\r\n    <parent link=\"torso_link\"/>\r\n    <child link=\"imu_link\"/>\r\n  </joint>\r\n  <link name=\"logo_link\">\r\n    <visual>\r\n      <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\r\n      <geometry>\r\n        <mesh filename=\"package://h1_description/meshes/logo_link.STL\"/>\r\n      </geometry>\r\n      <material name=\"\">\r\n        <color rgba=\"1 1 1 1\"/>\r\n      </material>\r\n    </visual>\r\n\r\n  </link>\r\n  <joint name=\"logo_joint\" type=\"fixed\">\r\n    <origin xyz=\"0 0 0\" rpy=\"0 0 0\"/>\r\n    <parent link=\"torso_link\"/>\r\n    <child link=\"logo_link\"/>\r\n","exit":[],"pics":[]},"tok":{"in":35786,"cached":33461,"out":110,"think":10,"usd":0.0007},"sim":{"submit":0,"reset":0}},{"id":20,"t":63.4,"say":"","calls":[{"kind":"robot","label":"a=np.tile(start,(35,1)); z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport json,numpy as np\nfrom rcb_client import Robot\nr=Robot(); start=r.spec()['start_command']['row'].copy(); start[1]=start[7]=-1.8; start[-2:]=[.4,.4]\na=np.tile(start,(35,1)); z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none')\nlay=z.meta['proprio']['layout']; x=z.meta['proprio']['now']\nfor n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_elbow_pitch_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','imu_pelvis_qw','imu_pelvis_qx','imu_pelvis_qy','imu_pelvis_qz','time_s']:\n print(n,round(x[lay.index(n)],4))\nprint('outcome',z.meta.get('reason'),z.meta.get('stages_reached'),z.meta.get('done'))\nz.save('/app/output/arm_test')\nPY"}],"out":{"text":"left_shoulder_pitch_pos -1.4652\nright_shoulder_pitch_pos -0.3814\nleft_elbow_pitch_pos 0.5831\nholding_left 0.0\nholding_right 0.0\nhand_closure_left 0.3515\nhand_closure_right 0.3454\nimu_pelvis_qw 1.0\nimu_pelvis_qx -0.0006\nimu_pelvis_qy 0.0099\nimu_pelvis_qz 0.0013\ntime_s 0.44\noutcome failed 0 True\n","exit":[],"pics":[]},"tok":{"in":40148,"cached":35783,"out":2125,"think":1845,"usd":0.002},"sim":{"submit":1,"reset":0}},{"id":21,"t":67.3,"say":"","calls":[{"kind":"shell","label":"j=json.load(open('/app/output/arm_test/meta.json'))","code":"python - <<'PY'\nimport json\nj=json.load(open('/app/output/arm_test/meta.json'))\nprint({k:v for k,v in j.items() if k not in ('proprio',)})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":42424,"cached":40145,"out":352,"think":262,"usd":0.0009},"sim":{"submit":0,"reset":0}},{"id":22,"t":72.7,"say":"The first trial ended early because my diagnostic changed a wrist target by mistake, and the ball fell. I’ve identified the row-index error. The reset budget remains intact; I’ll keep the wrist and inward squeeze angles fixed while I check a symmetric arm lift.","calls":[{"kind":"robot","label":"row=np.array(r.spec()['start_command']['row'],dtype=float); row[-2:]=[.45,.45]","code":"python - <<'PY'\nimport json,numpy as np\nfrom rcb_client import Robot\nr=Robot(); z=r.reset(cameras=['venue']); print('reset', {k:z.meta.get(k) for k in ['episode','resets_left','done','reason']})\nrow=np.array(r.spec()['start_command']['row'],dtype=float); row[-2:]=[.45,.45]\na=np.tile(row,(20,1)); z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print('close',[(n,round(x[lay.index(n)],3)) for n in ['holding_left','holding_right','hand_closure_left','hand_closure_right','hand_force_left','hand_force_right']])\nrow[1]=row[8]=-1.5\na=np.tile(row,(25,1));z=r.submit('stand',a,dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/lift_test')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print('lift',[(n,round(x[lay.index(n)],3)) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_shoulder_roll_pos','right_shoulder_roll_pos','left_elbow_pitch_pos','right_elbow_pitch_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','imu_pelvis_qx','imu_pelvis_qy','imu_pelvis_qz']]);print('end', {k:z.meta.get(k) for k in ['done','outcome','stages_reached','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":44114,"cached":42421,"out":1161,"think":668,"usd":0.0012},"sim":{"submit":2,"reset":1}},{"id":23,"t":83.6,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(15,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();r.reset();row=np.array(r.spec()['start_command']['row'],float);row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(15,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['holding_left','holding_right','hand_closure_left','hand_closure_right','hand_force_left','hand_force_right','left_hand_index_pos','right_hand_index_pos','left_hand_middle_pos','left_hand_thumb_yaw_pos','left_hand_thumb_pitch_pos','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nz.save('/app/output/grip_full')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":45579,"cached":44111,"out":1246,"think":994,"usd":0.0012},"sim":{"submit":1,"reset":1}},{"id":24,"t":88.5,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(20,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(20,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['holding_left','holding_right','hand_closure_left','hand_closure_right','hand_force_left','hand_force_right','left_hand_index_pos','right_hand_index_pos','left_hand_middle_pos','left_hand_thumb_yaw_pos','left_hand_thumb_pitch_pos','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nz.save('/app/output/grip_full2')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":46999,"cached":45576,"out":560,"think":310,"usd":0.0009},"sim":{"submit":1,"reset":0}},{"id":25,"t":92.9,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(25,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[1]=row[8]=-1.5;row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(25,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_shoulder_roll_pos','right_shoulder_roll_pos','left_shoulder_yaw_pos','right_shoulder_yaw_pos','left_wrist_yaw_pos','right_wrist_yaw_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','hand_force_left','hand_force_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left','stages_reached']});z.save('/app/output/lift_fullgrip')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":47733,"cached":46996,"out":573,"think":282,"usd":0.0008},"sim":{"submit":1,"reset":0}},{"id":26,"t":95.5,"say":"","calls":[{"kind":"look","label":"venue.jpg","code":"/app/output/lift_fullgrip/last/venue.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s026_0.jpg"]},"tok":{"in":48540,"cached":47730,"out":145,"think":103,"usd":0.0007},"sim":{"submit":0,"reset":0}},{"id":27,"t":100.7,"say":"","calls":[{"kind":"shell","label":"im=Image.open('/app/output/lift_fullgrip/last/venue.jpg')","code":"python - <<'PY'\nfrom PIL import Image\nim=Image.open('/app/output/lift_fullgrip/last/venue.jpg')\nim.crop((190,135,275,235)).resize((680,800)).save('/app/output/crop.png')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":48871,"cached":48537,"out":535,"think":436,"usd":0.0008},"sim":{"submit":0,"reset":0}},{"id":28,"t":102.4,"say":"","calls":[{"kind":"look","label":"crop.png","code":"/app/output/crop.png"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s028_0.jpg"]},"tok":{"in":49429,"cached":48868,"out":33,"think":0,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":29,"t":116.2,"say":"","calls":[{"kind":"robot","label":"r.submit('stand',np.tile(row,(35,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();r.reset();row=np.array(r.spec()['start_command']['row'],float);row[-2:]=[1.,1.]\nr.submit('stand',np.tile(row,(35,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none')\nrow[1]=row[8]=-.52\nz=r.submit('stand',np.array([row]),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/pitch_step1')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":50146,"cached":49426,"out":2313,"think":2044,"usd":0.0017},"sim":{"submit":2,"reset":1}},{"id":30,"t":119.6,"say":"","calls":[{"kind":"look","label":"venue.jpg","code":"/app/output/pitch_step1/last/venue.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s030_0.jpg"]},"tok":{"in":52579,"cached":50143,"out":134,"think":93,"usd":0.0009},"sim":{"submit":0,"reset":0}},{"id":31,"t":123.2,"say":"","calls":[{"kind":"shell","label":"im=Image.open('/app/output/pitch_step1/last/venue.jpg');im.crop((195,145,270,230)).resize((600,680)).save('/ap","code":"python - <<'PY'\nfrom PIL import Image\nim=Image.open('/app/output/pitch_step1/last/venue.jpg');im.crop((195,145,270,230)).resize((600,680)).save('/app/output/pitch_step1/crop.png')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":52899,"cached":52576,"out":109,"think":8,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":32,"t":125.3,"say":"","calls":[{"kind":"look","label":"crop.png","code":"/app/output/pitch_step1/crop.png"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s032_0.jpg"]},"tok":{"in":53031,"cached":52896,"out":37,"think":0,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":33,"t":133.6,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(5,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/o","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[1]=row[8]=-.52;row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(5,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/pitch_step2')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_elbow_pitch_pos','right_elbow_pitch_pos','holding_left','holding_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":53593,"cached":53028,"out":1154,"think":916,"usd":0.0012},"sim":{"submit":1,"reset":0}},{"id":34,"t":140.0,"say":"","calls":[{"kind":"shell","label":"for n in ['pitch_step1','pitch_step2']:","code":"python - <<'PY'\nfrom PIL import Image\nfor n in ['pitch_step1','pitch_step2']:\n im=Image.open('/app/output/'+n+'/last/venue.jpg'); im.crop((195,145,270,230)).resize((600,680)).save('/app/output/'+n+'/crop.png')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":54869,"cached":53590,"out":131,"think":20,"usd":0.0008},"sim":{"submit":0,"reset":0}},{"id":35,"t":142.3,"say":"","calls":[{"kind":"look","label":"crop.png","code":"/app/output/pitch_step2/crop.png"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s035_0.jpg"]},"tok":{"in":55023,"cached":54866,"out":37,"think":0,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":36,"t":148.4,"say":"The corrected symmetric arm command keeps the ball briefly, but the built in grasp sensor stays at zero. The fingers close while the ball remains against the palms. I’m stepping the shoulder pitch gradually and using the fixed camera to see where contact is lost.","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(20,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[1]=row[8]=-1.0;row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(20,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/pitch_step3')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_elbow_pitch_pos','right_elbow_pitch_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":55585,"cached":55020,"out":801,"think":495,"usd":0.001},"sim":{"submit":1,"reset":0}},{"id":37,"t":152.7,"say":"","calls":[{"kind":"shell","label":"im=Image.open('/app/output/pitch_step3/last/venue.jpg'); im.crop((195,130,280,230)).resize((680,800)).save('/a","code":"python - <<'PY'\nfrom PIL import Image\nim=Image.open('/app/output/pitch_step3/last/venue.jpg'); im.crop((195,130,280,230)).resize((680,800)).save('/app/output/pitch_step3/crop.png')\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":56530,"cached":55582,"out":154,"think":53,"usd":0.0008},"sim":{"submit":0,"reset":0}},{"id":38,"t":154.5,"say":"","calls":[{"kind":"look","label":"crop.png","code":"/app/output/pitch_step3/crop.png"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s038_0.jpg"]},"tok":{"in":56707,"cached":56527,"out":37,"think":0,"usd":0.0006},"sim":{"submit":0,"reset":0}},{"id":39,"t":158.7,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(5,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/o","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[1]=row[8]=-1.20;row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(5,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/pitch_step4')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_elbow_pitch_pos','right_elbow_pitch_pos','holding_left','holding_right','hand_closure_left','hand_closure_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":57428,"cached":56704,"out":512,"think":263,"usd":0.0009},"sim":{"submit":1,"reset":0}},{"id":40,"t":165.7,"say":"","calls":[{"kind":"robot","label":"z=r.submit('stand',np.tile(row,(10,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/","code":"python - <<'PY'\nimport numpy as np\nfrom rcb_client import Robot\nr=Robot();row=np.array(r.spec()['start_command']['row'],float);row[1]=row[8]=-1.20;row[-2:]=[1.,1.]\nz=r.submit('stand',np.tile(row,(10,1)),dt=.01,interpolate='hold',cameras=['venue'],video='none');z.save('/app/output/pitch_step5')\nlay=z.meta['proprio']['layout'];x=z.meta['proprio']['now'];print({n:round(x[lay.index(n)],3) for n in ['left_shoulder_pitch_pos','right_shoulder_pitch_pos','left_shoulder_pitch_target','right_shoulder_pitch_target','left_shoulder_pitch_torque','right_shoulder_pitch_torque','holding_left','holding_right','time_s']});print({k:z.meta.get(k) for k in ['done','outcome','reason','resets_left']})\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":58084,"cached":57425,"out":884,"think":631,"usd":0.0011},"sim":{"submit":1,"reset":0}},{"id":41,"t":289.6,"say":"The controlled lift held through a shoulder pitch near −1.0 rad; moving farther by itself broke contact around −1.1. I’m using the remaining two attempts for a coordinated squat, leg extension, and arm swing, then a walking-policy landing. The downloaded Unitree H1 URDF is only an arm-axis reference; no controller was downloaded.","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":59125,"cached":58081,"out":21234,"think":19667,"usd":0.0113},"sim":{"submit":0,"reset":0}},{"id":42,"t":300.7,"say":"","calls":[{"kind":"shell","label":"sed -n '1,260p' /app/output/gen.py && cat /app/NOTES.md","code":"sed -n '1,260p' /app/output/gen.py && cat /app/NOTES.md"}],"out":{"text":"\"\"\"Open-loop controller for the Embodied Olympics H1-2 dunk instance.\"\"\"\nfrom __future__ import annotations\n\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nfrom rcb_client import Robot\n\n\nOUT = Path(__file__).resolve().parent\n\n\ndef say(label, result):\n    m = result.meta\n    print(label, json.dumps({k: m.get(k) for k in (\n        \"sim_time_s\", \"done\", \"outcome\", \"reason\", \"stages_reached\",\n        \"stages_total\", \"resets_left\", \"executed_rows\", \"clipped\")\n    }))\n\n\ndef run(robot, kind, row, seconds, label):\n    n = round(seconds / 0.01)\n    actions = np.tile(np.asarray(row, dtype=float), (n, 1))\n    result = robot.submit(kind, actions, dt=0.01, interpolate=\"hold\",\n                          cameras=[\"venue\"], video=\"none\")\n    result.save(str(OUT / label))\n    say(label, result)\n    return result\n\n\ndef main():\n    robot = Robot()\n    spec = robot.spec()\n    (OUT / \"spec.json\").write_text(json.dumps(spec, indent=2, default=str))\n    if robot.status().get(\"done\"):\n        obs = robot.reset(cameras=[\"venue\"])\n        say(\"reset\", obs)\n    print(\"connected\", robot.status())\n    start = np.asarray(spec[\"start_command\"][\"row\"], dtype=float)\n\n    # Close around the ball while keeping the initial inward palm squeeze.\n    start[-2:] = 1.0\n    result = run(robot, \"stand\", start, 0.35, \"attempt_close\")\n    if result.done:\n        return\n\n    # Raise the ball to the forward carrying pose. Wrist and squeeze angles stay put.\n    carry = start.copy()\n    carry[1] = carry[8] = -1.0\n \n… (929 characters left out) …\nmp[12] = 0.12\n    jump[[13, 20]] = -2.50\n    jump[[16, 23]] = 1.05\n    result = run(robot, \"joints\", jump, 0.22, \"attempt_takeoff\")\n    if result.done:\n        return\n    result = run(robot, \"joints\", jump, 0.30, \"attempt_peak\")\n    if result.done:\n        return\n\n    # Lower the ball toward the basket while the body descends.\n    dunk = jump.copy()\n    dunk[[13, 20]] = -2.20\n    dunk[[16, 23]] = 1.15\n    result = run(robot, \"joints\", dunk, 0.16, \"attempt_dunk_1\")\n    if result.done:\n        return\n    dunk[[13, 20]] = -2.00\n    dunk[[16, 23]] = 1.25\n    result = run(robot, \"joints\", dunk, 0.16, \"attempt_dunk_2\")\n    if result.done:\n        return\n    dunk[[13, 20]] = -1.80\n    dunk[[16, 23]] = 1.35\n    result = run(robot, \"joints\", dunk, 0.16, \"attempt_dunk_3\")\n    if result.done:\n        return\n\n    # The center should now be entering the ring; release and stabilize on the walker.\n    walk = np.zeros(20, dtype=float)\n    walk[3] = 0.0\n    result = run(robot, \"walk\", walk, 2.80, \"attempt_land\")\n    if result.done:\n        return\n    result = robot.get_obs(cameras=[\"venue\"], video=\"none\")\n    result.save(str(OUT / \"attempt_final_obs\"))\n    say(\"attempt_final_obs\", result)\n\n\nif __name__ == \"__main__\":\n    main()\n# Dunk attempt notes\n\n- Controller entry point: `output/gen.py`.\n- Starting from the service spec and robot camera/proprioception only.\n- No external code, models, or packages downloaded or installed.\n- Approach and attempt outcomes will be updated after observations.\n","exit":[],"pics":[]},"tok":{"in":80385,"cached":59122,"out":570,"think":507,"usd":0.0035},"sim":{"submit":0,"reset":0}},{"id":43,"t":306.1,"say":"","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":82156,"cached":80382,"out":368,"think":152,"usd":0.0012},"sim":{"submit":0,"reset":0}},{"id":44,"t":308.7,"say":"","calls":[{"kind":"robot","label":"python /app/output/gen.py","code":"python /app/output/gen.py"}],"out":{"text":"reset {\"sim_time_s\": 0.0, \"done\": false, \"outcome\": null, \"reason\": null, \"stages_reached\": null, \"stages_total\": null, \"resets_left\": 1, \"executed_rows\": null, \"clipped\": null}\nconnected {'calls': 19, 'rows': 198, 'control_steps': 0, 'done': False, 'reason': None, 'episode': 5, 'control_steps_total': 198, 'resets_left': 1, 'elapsed_s': 303.0}\nattempt_close {\"sim_time_s\": 0.35, \"done\": false, \"outcome\": null, \"reason\": null, \"stages_reached\": null, \"stages_total\": null, \"resets_left\": 1, \"executed_rows\": 35, \"clipped\": []}\nattempt_carry {\"sim_time_s\": 0.5, \"done\": true, \"outcome\": \"the ball left the hands before the dunk\", \"reason\": \"failed\", \"stages_reached\": 0, \"stages_total\": 2, \"resets_left\": 1, \"executed_rows\": 15, \"clipped\": []}\n","exit":[],"pics":[]},"tok":{"in":82548,"cached":82153,"out":145,"think":98,"usd":0.0009},"sim":{"submit":2,"reset":1}},{"id":45,"t":320.2,"say":"","calls":[{"kind":"look","label":"venue.jpg","code":"/app/output/attempt_carry/last/venue.jpg"}],"out":{"text":"","exit":[],"pics":["embodied-olympics-fun-dunk-i00-standard--ngifgj5/s045_0.jpg"]},"tok":{"in":82982,"cached":82545,"out":1514,"think":1473,"usd":0.0016},"sim":{"submit":0,"reset":0}},{"id":46,"t":382.7,"say":"","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":84682,"cached":82979,"out":8883,"think":7842,"usd":0.0055},"sim":{"submit":0,"reset":0}},{"id":47,"t":399.8,"say":"","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":93589,"cached":84679,"out":691,"think":487,"usd":0.0023},"sim":{"submit":0,"reset":0}},{"id":48,"t":407.9,"say":"","calls":[{"kind":"robot","label":"python /app/output/gen.py","code":"python /app/output/gen.py"}],"out":{"text":"reset {\"sim_time_s\": 0.0, \"done\": false, \"outcome\": null, \"reason\": null, \"stages_reached\": null, \"stages_total\": null, \"resets_left\": 0, \"executed_rows\": null, \"clipped\": null}\nconnected {'calls': 22, 'rows': 248, 'control_steps': 0, 'done': False, 'reason': None, 'episode': 6, 'control_steps_total': 248, 'resets_left': 0, 'elapsed_s': 402.2}\nattempt_close {\"sim_time_s\": 0.35, \"done\": false, \"outcome\": null, \"reason\": null, \"stages_reached\": null, \"stages_total\": null, \"resets_left\": 0, \"executed_rows\": 35, \"clipped\": []}\nattempt_squat {\"sim_time_s\": 0.62, \"done\": true, \"outcome\": \"the ball left the hands before the dunk\", \"reason\": \"failed\", \"stages_reached\": 0, \"stages_total\": 2, \"resets_left\": 0, \"executed_rows\": 27, \"clipped\": []}\n","exit":[],"pics":[]},"tok":{"in":94304,"cached":93586,"out":555,"think":508,"usd":0.0013},"sim":{"submit":2,"reset":1}},{"id":49,"t":428.8,"say":"The final attempt dropped the ball during the squat, before takeoff. All five resets are now used, so I’m recording the result and checking the service’s final status.","calls":[{"kind":"shell","label":"python - <<'PY'","code":"python - <<'PY'\nfrom rcb_client import Robot\nr=Robot();print(r.status())\nPY"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":95148,"cached":94301,"out":1456,"think":1349,"usd":0.0018},"sim":{"submit":0,"reset":0}},{"id":50,"t":434.6,"say":"","calls":[{"kind":"edit","label":"a file","code":"patch));\n"}],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":96688,"cached":95145,"out":377,"think":134,"usd":0.0013},"sim":{"submit":0,"reset":0}},{"id":51,"t":446.2,"say":"The dunk attempt did not succeed. The final episode ended at 0.62 s during the squat when the ball left the hands; the referee reports 0 of 2 stages reached. All five resets are used.\n\nI saved the controller at [gen.py](/app/output/gen.py) and recorded the attempts and outcome in [NOTES.md](/app/NOTES.md).","calls":[],"out":{"text":"","exit":[],"pics":[]},"tok":{"in":97089,"cached":96685,"out":305,"think":221,"usd":0.0012},"sim":{"submit":0,"reset":0}}]}