Robot Learning

One-Shot Imitation Learning

One-shot imitation learning is the problem of executing a new task after seeing a single demonstration of it, rather than training on a large task-specific dataset. Approaches meta-train across distributions of tasks so the policy can condition on a demonstration at test time, as in the one-shot imitation work of Duan et al. and meta-learning approaches following MAML. Recent variants condition generalist policies on demonstration videos or trajectory prompts.

Why it matters for physical AI

Teaching a robot a new task from one showing, instead of hundreds of teleoperated episodes, would change the economics of deployment, making it a persistent benchmark for generalist policies.