{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/read-retrieval-enhanced-asymmetric-diffusion","title":"READ: Retrieval-Enhanced Asymmetric Diffusion for Motion Planning","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Takeru Oba","Matthew Walter","Norimichi Ukita"],"abstract":"    This paper proposes Retrieval-Enhanced Asymmetric Diffusion (READ) for image-based robot motion planning. Given an image of the scene READ retrieves an initial motion from a database of image-motion pairs and uses a diffusion model to refine the motion for the given scene. Unlike prior retrieval-based diffusion models that require long forward-reverse diffusion paths READ directly diffuses between the source (retrieved) and target motions resulting in an efficient diffusion path. A second contribution of READ is its use of asymmetric diffusion whereby it preserves the kinematic feasibility of the generated motion by forward diffusion in a low-dimensional latent space while achieving high-resolution motion by reverse diffusion in the original task space using cold diffusion. Experimental results on various manipulation tasks demonstrate that READ outperforms state-of-the-art planning methods while ablation studies elucidate the contributions of asymmetric diffusion.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Oba_READ_Retrieval-Enhanced_Asymmetric_Diffusion_for_Motion_Planning_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Oba_READ_Retrieval-Enhanced_Asymmetric_Diffusion_for_Motion_Planning_CVPR_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"read-retrieval-enhanced-asymmetric-diffusion","repo_url":"https://github.com/obat2343/read","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"motion-planning","task_name":"Motion Planning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}