imitation-learning
2 free lessons tagged imitation-learning across Robotics. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Action Chunking and Diffusion Policies
Two ideas that made imitation learning work: predicting a chunk of future actions instead of one step (ACT, from the ALOHA system) to blunt compounding error, and generating actions by denoising (Diffusion Policy) to capture the many valid ways to do a task. Plus the pooled multi-robot datasets, like Open X-Embodiment, that set up generalist policies.
From Control to Imitation Learning
Why modern robots learn skills from demonstrations instead of hand-written controllers. This lesson covers behavior cloning (supervised observation-to-action learning), the distribution-shift and compounding-error problem that makes it fragile, DAgger as the classic fix, and how teleoperated demonstrations became the fuel for robot learning.