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sreetz-nv
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so101_vials_actuators_sysid

Tasks:
Robotics
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parquet
Size:
10K - 100K
Tags:
LeRobot
so101
sim2real
isaac-lab
newton
sysid
Libraries:
Datasets
pandas
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License:
Dataset card Data Studio Files
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SO-101 vials-to-rack, SysID (AnchorBench) actuator fit, 100 episodes

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Files changed (9) hide show
  1. README.md +74 -0
  2. data/chunk-000/file-000.parquet +3 -0
  3. meta/episodes/chunk-000/file-000.parquet +3 -0
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  8. videos/observation.images.external/chunk-000/file-001.mp4 +3 -0
  9. videos/observation.images.wrist/chunk-000/file-000.mp4 +3 -0
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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - robotics
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+ tags:
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+ - LeRobot
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+ - so101
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+ - sim2real
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+ - isaac-lab
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+ - newton
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+ - sysid
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+ configs:
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+ - config_name: default
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+ data_files: data/*/*.parquet
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+ ---
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+
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+ # SO-101 vials-to-rack — SysID actuator fit
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+
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+ **This is the SysID arm.** Actuator parameters resolve from the SO-101 asset's
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+ `physics` variant, which authors the AnchorBench system-identification fit.
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+
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+ Paired counterpart: [`sreetz-nv/so101_vials_actuators_hand_tuned`](https://huggingface.co/datasets/sreetz-nv/so101_vials_actuators_hand_tuned),
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+ identical in every respect except that it substitutes the workshop's pre-SysID
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+ hand-tuned per-joint gains. The two were collected back to back for a
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+ controlled actuator-fit A/B; use them together or the comparison is lost.
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+
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+ ## What it is
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+
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+ 100 scripted-success episodes of the SO-101 arm picking a vial off a mat and
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+ placing it in a yellow tube rack, recorded in Isaac Lab with Newton/MJWarp
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+ physics and the OVRtx renderer. Task instruction: "Pick up the vial and place
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+ it in the rack."
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+
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+ | | |
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+ |---|---|
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+ | episodes | 100 |
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+ | frames | 33,441 |
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+ | mean frames/episode | 334 |
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+ | fps | 30 |
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+ | robot type | `so101_follower` |
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+ | cameras | wrist + external, 480x640 RGB |
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+ | environment | `Newton-So101-Auto-Vials-State-Machine-DR-v0` (domain randomized) |
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+ | collection | scripted state machine; only episodes accepted by the `success` termination term were kept |
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+
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+ ## Actuator parameters (the variable under test)
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+
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+ Resolved per joint from the asset, not set in Python:
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+
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+ | joint | stiffness | damping | armature | static friction | viscous friction | effort limit | velocity limit |
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+ |---|---|---|---|---|---|---|---|
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+ | shoulder_pan | 48.42 | 3.335 | 0.0676 | 0.347 | 1.591 | 3.35 | 30 |
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+ | shoulder_lift | 47.78 | 2.563 | 0.0276 | 0.345 | 0.561 | 3.35 | 30 |
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+ | elbow_flex | 14.72 | 0.343 | 0.0377 | 0.411 | 0.797 | 3.35 | 30 |
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+ | wrist_flex | 43.61 | 4.139 | 0.0507 | 0.248 | 1.072 | 3.35 | 30 |
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+ | wrist_roll | 54.87 | 2.968 | 0.0549 | 0.222 | 1.638 | 3.35 | 30 |
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+ | gripper | 68.25 | 2.819 | 0.0776 | 0.083 | 0.959 | 3.35 | 30 |
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+
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+ The grasp force servo is paired to the 68.25 N.m/rad gripper drive with gain
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+ 0.066 rad/(N.s) and a 0.198 rad/s jaw rate cap.
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+
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+ ## Held constant against the paired dataset
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+
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+ Same USD asset (`so101_new_calib_SysID`) with the same `Robot`/`Sensor`/`Physics`
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+ variants, same collision geometry, same contact material, same wrist camera and
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+ intrinsics, same solver and `nconmax=512`, same task, same domain
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+ randomization. Only the drive parameters differ.
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+
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+ ## Caveats
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+
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+ - Sim only. Nothing here speaks to real-robot transfer.
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+ - The paired datasets have different episode lengths (334 against 290 mean
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+ frames), so a fixed step budget gives the two arms different epoch counts.
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+ - Scripted acceptance filters scene difficulty; the acceptance rate differs
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+ between the paired arms.
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