Diffusion Policy

Diffusion policies learn to turn observations such as images, point clouds, and touch into action sequences, often representing several plausible ways to act rather than a single average behavior. Work commonly trains them through imitation of demonstrations, then adapts them with reinforcement learning, world models, or inference-time guidance; action chunking and multimodal sensing are widely used for manipulation, navigation, and whole-body control. Evaluations span simulated manipulation benchmarks and physical robots, including contact-rich and long-horizon tasks. A central challenge is retaining expressive, robust behavior while reducing denoising latency and improving feedback, safety, and generalization to new environments.