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

Ablation	Step	Train std. MAE	Eval std. MAE	Test std. MAE	Train-test gap	Selected
act-front-top	15000	0.101565	0.175120	0.191440	0.089874	no
act-front-top-left	10000	0.117780	0.173132	0.191308	0.073528	yes
smolvla-front-top	20000	0.130488	0.206360	0.231821	0.101333	no

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.

Split	Episodes	Frames
train	118	22424
eval	15	2850
test	15	2849

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.