{"work":{"id":"2dce18e6-f07a-4f57-8a81-e71c3e6a293c","openalex_id":null,"doi":null,"arxiv_id":"2303.04137","raw_key":null,"title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","authors":null,"authors_text":"Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel","year":2023,"venue":"cs.RO","abstract":"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","external_url":"https://arxiv.org/abs/2303.04137","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-25T07:35:29.418170+00:00","pith_arxiv_id":"2303.04137","created_at":"2026-05-09T06:10:42.430902+00:00","updated_at":"2026-05-25T07:35:29.418170+00:00","title_quality_ok":true,"display_title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","render_title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion"},"hub":{"state":{"work_id":"2dce18e6-f07a-4f57-8a81-e71c3e6a293c","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":54,"external_cited_by_count":null,"distinct_field_count":5,"first_pith_cited_at":"2023-05-22T17:57:41+00:00","last_pith_cited_at":"2026-05-22T17:08:37+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-30T23:31:41.603474+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":10},{"context_role":"method","n":7},{"context_role":"baseline","n":1},{"context_role":"dataset","n":1},{"context_role":"extension","n":1}],"polarity_counts":[{"context_polarity":"background","n":9},{"context_polarity":"use_method","n":7},{"context_polarity":"baseline","n":1},{"context_polarity":"extend","n":1},{"context_polarity":"unclear","n":1},{"context_polarity":"use_dataset","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}