piper-left-clean-rad-v1-act-front-top-left

PranayTest/piper-left-clean-rad-v1-act-front-top-left

This is the selected offline imitation-learning checkpoint for the PiPER left-arm plate pick-and-lift task. It was selected from parallel ACT and SmolVLA ablations using evaluation action-prediction error and the requested train-test divergence criterion.

Selected checkpoint

• Run: act-front-top-left-chunk40
• Ablation label: act-front-top-left
• Training step: 10000
• Train standardized MAE: 0.117780
• Eval standardized MAE: 0.173132
• Test standardized MAE: 0.191308
• Absolute train-test divergence: 0.073528
• Test joint MAE: 0.072642 radians
• Test gripper MAE: 0.243458
• W&B project: https://wandb.ai/deepanshu_rohilla-mrfood-ai/lerobot-piper-clean-v1

Candidate comparison

Checkpoint selection within each run used minimum eval standardized MAE. The cross-run comparison Pareto-minimized eval standardized MAE and absolute train-test divergence, with their sum as a deterministic frontier tie-break. Because that criterion inspects the test split, the test result influenced model-family selection and is not an untouched final audit. A new task-level dataset or physical rollout campaign is required for a final unbiased audit.

Dataset and preprocessing

• Source: PranayTest/classified-data-2026-07-06
• Pinned revision: 8ad2dc39b271c3573b2721526f95c10a208c72ba
• Quality JSONL SHA-256: ff6323b522fb4612c259a5e839c6919619c928957ca929d3bab7110ec5cb343d
• Selected source episodes: 148
• Filter: class == good, confidence >= 0.9, camera quarantine [167, 217], and right-arm command range <= 2.0 degrees
• Cameras: front, top, left
• Output state/action order: left_joint1, left_joint2, left_joint3, left_joint4, left_joint5, left_joint6, left_gripper
• Joints: converted from degrees to radians
• Gripper: normalized [0, 1]
• Actions: absolute commanded targets
• Camera timestamps: excluded from policy inputs
• Split strategy: chronological_original_episode_order_80_10_10
• The derived 7D-radian datasets were materialized locally from the pinned source; the exact selection manifest and builder/evaluation code are bundled with this model release.

Intended use and limitations

This checkpoint is for offline research and staged robot-policy integration. The reported errors measure action prediction on recorded demonstrations; they are not physical task-success rates and do not establish collision safety.

Before any robot execution, use a dedicated inference adapter with fresh-state checks, controller-level joint/rate/acceleration limits, reject-and-hold behavior, fault and temperature checks, collision safeguards, an operator E-stop, and a staged ghost/dry-run/live rollout process. The model is specific to a left-arm plate pick-and-lift setup and should not be assumed to generalize to other objects, layouts, cameras, robots, or bimanual behavior.