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:
0.072642radians - 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 <=2.0degrees - 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.