Robot Learning

Imitation Learning

Imitation learning is the paradigm of training policies from expert demonstrations rather than from reward signals, most simply via behavior cloning, which regresses actions from observations, and more robustly via interactive methods like DAgger or distribution-matching approaches like GAIL. Teleoperated demonstration collection combined with expressive policy classes, as in ALOHA's ACT and Diffusion Policy, drives most current real-robot manipulation results.

Why it matters for physical AI

Demonstrations sidestep reward engineering and unsafe exploration, making imitation the workhorse for bootstrapping manipulation skills and the pretraining substrate for robot foundation models.