Robot Learning

Behavior Cloning (BC)

Behavior cloning is the simplest form of imitation learning: a policy is trained by supervised learning to map observations to the actions an expert took, treating demonstrations as a labeled dataset. Its central weakness is compounding error, since small mistakes drift the robot into states absent from training data, formalized by Ross et al.'s DAgger analysis. Modern remedies include action chunking, expressive generative action heads, and simply scaling data, which together power systems from ACT to vision-language-action models.

Why it matters for physical AI

Nearly all current robot foundation models are trained substantially by behavior cloning on teleoperated demonstrations, so its data appetite and distribution-shift failure modes define the field's core scaling economics.