[Submitted on 10 Sep 2026]
Title:ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations
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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)
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