Robot Learning

Learning from Demonstration (LfD)

Learning from demonstration (LfD) is the umbrella paradigm in which robots acquire skills from examples provided by a teacher, most often via teleoperation or kinesthetic guidance, instead of explicit programming or trial-and-error reward optimization. Methods span behavior cloning, dynamic movement primitives, inverse reinforcement learning, and modern transformer or diffusion policies trained on demonstration corpora such as those collected with ALOHA-style rigs.

Why it matters for physical AI

Demonstrations remain the dominant supervision source for robot foundation models; nearly every large-scale manipulation dataset, from RT-1 to Open X-Embodiment, is built on human-provided examples of successful task execution.