Adding interpretable reasoning traces to robot training data and fine-tuning existing models to use them can boost zero-shot robot task performance by up to 82 percentage points—without collecting new robot demonstrations or scaling up models.
This paper introduces ARC, a method to improve robot foundation models by adding reasoning traces—explanations of why robots take actions and what effects they should produce.