ObstaDiff: 障害物認識表現による汎化可能な拡散ポリシー学習
ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations
障害物を意識した視覚表現を組み込んだ拡散ポリシーを提案し、雑然とした農業環境でのロボットマニピュレーションの汎化性能を向上させた。
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著者: Jiawen Wang, Kevin Yao, Khalid Jawed
分類: cs.RO, cs.LG
原文アブストラクト
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.