This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it 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. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is publicly available diffusion-policy.cs.columbia.edu

翻译：本文提出了扩散策略（Diffusion Policy），这是一种通过将机器人的视觉运动策略表示为条件去噪扩散过程来生成机器人行为的新方法。我们在来自4个不同机器人操作基准的12个不同任务上对扩散策略进行了基准测试，发现其始终优于现有的最先进机器人学习方法，平均改进幅度达46.9%。扩散策略学习动作分布得分函数的梯度，并在推理过程中通过一系列随机Langevin动力学步骤相对于该梯度场进行迭代优化。我们发现，扩散框架在用于机器人策略时展现出强大优势，包括优雅地处理多模态动作分布、适用于高维动作空间以及表现出卓越的训练稳定性。为了在物理机器人上充分释放扩散模型在视觉运动策略学习中的潜力，本文提出了一系列关键技术贡献，包括引入滚动时域控制、视觉条件以及时间序列扩散Transformer。我们希望这项工作能推动新一代策略学习技术的发展，充分利用扩散模型强大的生成建模能力。代码、数据和训练细节已公开于diffusion-policy.cs.columbia.edu。