Policy Guides¶
AM-Bench provides task-aware scripted policies and three maintained learned-policy integrations.
| Policy | Best use | Training data | Evaluation process |
|---|---|---|---|
| Scripted | task validation and demonstration collection | none | in simulator |
| ACT | compact task-specific imitation baseline | canonical LeRobot | in simulator process |
| Diffusion Policy | task-specific image-conditioned diffusion baseline | derived UMI zarr | in simulator process |
| PI0 / PI0.5 | pretrained VLA adaptation and language-conditioned evaluation | pinned OpenPI export | separate policy server and simulator client |
Shared contract¶
All learned policies begin from a validated canonical dataset and must match the environment's action semantics. Evaluators share rollout controls, progress output, artifact naming, tracking, and video behavior. See Train and Evaluate for the common boundary.
Start small¶
Before a full experiment:
- validate the source dataset at the intended policy rate;
- run a bounded training smoke that saves a loadable checkpoint;
- evaluate one rollout with one environment and video enabled;
- inspect
results.txt,eval_summary.json, tracking analysis, and all requested camera videos; - scale only after the complete pipeline passes.
Training success does not prove evaluation compatibility, and a successful one-rollout smoke does not measure policy quality.