Diffusion Policy rethinks behavior cloning by treating policy learning as a generative modeling problem. Rather than predicting a single deterministic action or fitting a unimodal Gaussian, the method trains a denoising diffusion model to generate a coherent action sequence conditioned on the current visual observation. Starting from random noise, the model iteratively refines its output over a series of denoising steps until a smooth, feasible action chunk emerges. This formulation elegantly solves a long-standing problem in imitation learning: how to represent multimodal action distributions without suffering from averaging artifacts. In traditional behavior cloning, when the demonstration dataset contains multiple valid strategies for the same scenario—such as pushing an object from the left versus the right—a deterministic policy tends to average these options into a meaningless compromise. Diffusion Policy avoids this by learning the full distribution of demonstrated actions. Because diffusion models are inherently multimodal, they can capture diverse valid strategies and sample from them at test time. Additionally, the model predicts short action horizons (e.g., 8 or 16 future timesteps) rather than isolated instantaneous actions. This chunk-based prediction leads to significantly smoother and more temporally consistent robot behavior, reducing jerk and mechanical wear. The architecture is surprisingly flexible. The authors show that the denoising network can be implemented as a convolutional U-Net, a transformer, or even a simple MLP, depending on the observation and action space. Training follows the standard conditional diffusion objective: given an observation, the model learns to reverse the noising process applied to the corresponding action chunk from the demonstration dataset. At inference time, the policy runs a fixed number of denoising iterations (typically 10 to 100) to generate the final action sequence. The paper also demonstrates that inference acceleration techniques such as DDIM can cut computational cost with minimal impact on task success. Empirical evaluation spans both simulated benchmarks and real hardware. On the classic Push-T contact-rich manipulation task, Diffusion Policy outperforms prior state-of-the-art methods including ACT and VINN by a wide margin. It also achieves strong results on the Franka Kitchen multi-task benchmark and a suite of real-world robot tasks such as pouring and slot insertion. The hardware experiments confirm that the smooth, multimodal actions generated by diffusion translate directly into higher success rates and more robust behavior on physical robots.