fine-tuning-serving-openpi

You fine-tune OpenPI robot policies on custom datasets by using the JAX backend for training, which requires precomputed normalization statistics before initiating the adaptation process.

You can convert JAX checkpoints to PyTorch for pi0 model deployment using the provided conversion workflows, enabling a dual-backend setup where JAX handles training and PyTorch runs the policy inference server.

OpenPI policy serving supports real-time robot policy inference across ALOHA, DROID, and LIBERO environments, allowing you to deploy adapted pi0, pi0-fast, and pi0.5 models for evaluation.

Yes, you need precomputed normalization statistics before training or running inference with OpenPI models, as accurate norm statistics are required to ensure proper policy adaptation and serving.

OpenPI policy training encounters GPU memory issues due to the large size of pi0 models during the JAX fine-tuning process, requiring specific memory debugging workflows to successfully adapt the robot policies.

The best way to run a robot policy inference server is to deploy converted PyTorch OpenPI checkpoints, which provides real-time policy inference across target environments like ALOHA, DROID, and LIBERO.