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We benchmark Diffusion Policy across 15 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 are available (diffusion-policy.cs.columbia.edu).<\/jats:p>","DOI":"10.1177\/02783649241273668","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T08:17:02Z","timestamp":1728634622000},"page":"1684-1704","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":308,"title":["Diffusion policy: Visuomotor policy learning via action diffusion"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0319-0228","authenticated-orcid":false,"given":"Cheng","family":"Chi","sequence":"first","affiliation":[{"name":", Columbia University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8217-4818","authenticated-orcid":false,"given":"Zhenjia","family":"Xu","sequence":"additional","affiliation":[{"name":", Columbia University"}]},{"given":"Siyuan","family":"Feng","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-8046","authenticated-orcid":false,"given":"Eric","family":"Cousineau","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}]},{"given":"Yilun","family":"Du","sequence":"additional","affiliation":[{"name":", MIT"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7332-6712","authenticated-orcid":false,"given":"Benjamin","family":"Burchfiel","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"}]},{"given":"Russ","family":"Tedrake","sequence":"additional","affiliation":[{"name":"Toyota Research Institute"},{"name":", MIT"}]},{"given":"Shuran","family":"Song","sequence":"additional","affiliation":[{"name":", Columbia University"},{"name":", Stanford University"}]}],"member":"179","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"e_1_3_5_2_1","unstructured":"Ajay A Du Y Gupta A et al. 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