Diffusion Policy is a new method for generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. The method is benchmarked across 15 tasks from four different robot manipulation benchmarks and consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. The diffusion formulation yields powerful advantages for robot policies, including graceful handling of multimodal action distributions, suitability for high-dimensional action spaces, and impressive training stability. The paper presents key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. The code, data, and training details are available at diffusion-policy.cs.columbia.edu.Diffusion Policy is a new method for generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. The method is benchmarked across 15 tasks from four different robot manipulation benchmarks and consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. The diffusion formulation yields powerful advantages for robot policies, including graceful handling of multimodal action distributions, suitability for high-dimensional action spaces, and impressive training stability. The paper presents key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. The code, data, and training details are available at diffusion-policy.cs.columbia.edu.