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name: policy-training description: "Routes ACT, CNNMLP, Diffusion Policy, and latent-model training or evaluation workflows for ACT++ checkpoints and datasets." disable-model-invocation: true metadata: disco-role: operating license: MIT

policy-training

Use this sub-skill when the task is about training or evaluating ACT++, CNNMLP, Diffusion Policy, or the VQ latent model that follows ACT training.

Typical triggers

• "Train ACT on sim_transfer_cube_scripted"
• "Evaluate the best checkpoint"
• "Why does policy.py fail to import?"
• "What does --temporal_agg do?"
• "How do I run the latent model training pass?"

What this sub-skill covers

• Step-based training and evaluation through the imitate_episodes workflow.
• ACT, CNNMLP, and Diffusion policy wrappers.
• The DETR-derived backbone / transformer / latent model internals that drive those wrappers.
• Dataset loading, normalization, and evaluation statistics.
• VQ latent model training from a previously trained ACT checkpoint.

What it excludes

• Simulated data generation, replay, and rendering -> simulation-data.
• VINN feature cache and k-selection -> vinn-offline.
• Real robot deployment or ROS/servo control -> root troubleshooting only.
• Experimental actuator-network training with hard-coded paths -> reference-only note only.

Read these first

• Workflow recipes
• Model overview
• Troubleshooting
• API reference
• Data formats

Run this helper first

Before trying to train, use check_policy_stack.py to confirm the repo checkout imports, the CUDA backend is visible, and the policy wrapper surface is present.