fine-tuning-serving-openpi

To fine-tune OpenPI models, you prepare custom datasets for environments like ALOHA, DROID, or LIBERO, run deterministic training steps, convert checkpoints, and deploy via WebSocket policy serving for real-time robot inference.

Yes, OpenPI fine-tuning supports both JAX and PyTorch backends, allowing you to train and serve robot policies across multi-env deployments using either on-disk or remote checkpoints.

The best way to serve OpenPI policies is through WebSocket policy serving, which enables real-time inference for deployed robots or simulators after converting your fine-tuned checkpoints.

Yes, OpenPI fine-tuning explicitly supports ALOHA, DROID, and LIBERO environments, enabling you to adapt pi0, pi0-fast, and pi0.5 models to custom datasets across these robotics platforms.

Deterministic training steps and norm stats ensure consistent, reproducible model adaptation when fine-tuning OpenPI policies, preventing variance in robot behavior during real-time inference deployment.

Yes, checkpoint conversion is required to transform fine-tuned OpenPI models into a deployable format, enabling real-time WebSocket policy serving on robots or simulators.