Its two-stage recipe first learns from broad manipulation data and then aligns the policy with target embodiments through post-training. The release includes code and checkpoints for adapting the policy, running inference and comparing performance in a benchmark.
Robotics researchers can use the released assets to test task execution or fine-tune on their own robot data. The setup separates model experimentation from deployment steps that require a compatible embodiment and a controlled physical environment.
