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[Submitted on 10 Sep 2026] Title:ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations View a PDF of the paper titled ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations, by Jiawen Wang and 2 other authors View PDF HTML (experimental) Abstract:Imitation learning has achieved impressive results in robotic manipulation, yet most existing approaches assume clean backgrounds and lack explicit mechanisms for obstacle-aware motion generation. Extending such policies to cluttered, real-world scenes with unstructured obstacles remains a key generalization challenge. We present ObstaDiff, a decomposed diffusion-policy framework with a lightweight obstacle-aware visual encoder. ObstaDiff extracts a structured target-obstacle-background representation, enabling the downstream alignment policy to generate end-effector trajectories toward a target-centered bottleneck pose while reasoning about surrounding obstacles. We evaluate ObstaDiff on 61 real-robot greenhouse trials per method (366 executions in total). ObstaDiff achieves 75.41% average task success and 8.20% average obstacle collision rate, outperforming representative imitation-learning baselines and improving generalization in cluttered agricultural scenes. Comments: Accepted to the 10th Conference on Robot Learning (CoRL 2026), Austin, TX, USA. 16 pages, 5 figures Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2609.10918 [cs.RO] (or arXiv:2609.10918v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.10918 arXiv-issued DOI via DataCite (pending registration) Submission history From: Jiawen Wang [view email] [v1] Thu, 10 Sep 2026 00:06:39 UTC (1,294 KB) Full-text links: Access Paper: View a PDF of the paper titled ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations, by Jiawen Wang and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)