ObstaDiff：基于障碍物感知表示的泛化扩散策略学习
ObstaDiff: Generalizable Diffusion Policy Learning via Obstacle-aware Representations
- University of California, Los Angeles（加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
ObstaDiff提出分解扩散策略框架，利用轻量级障碍物感知编码器提取目标-障碍物-背景表示，在真实温室试验中实现75.41%成功率和8.20%碰撞率，提升杂乱场景泛化能力。
AI中文摘要：
模仿学习在机器人操作中取得了显著成果，但现有方法大多假设背景干净，且缺乏针对障碍物感知运动生成的显式机制。将此类策略扩展到具有非结构化障碍物的杂乱真实世界场景中，仍然是一个关键的泛化挑战。我们提出了ObstaDiff，一种分解的扩散策略框架，配备轻量级障碍物感知视觉编码器。ObstaDiff提取结构化的目标-障碍物-背景表示，使下游对齐策略能够生成朝向以目标为中心的瓶颈姿态的末端执行器轨迹，同时推理周围障碍物。我们在每种方法61次真实机器人温室试验（共366次执行）上评估了ObstaDiff。ObstaDiff实现了75.41%的平均任务成功率和8.20%的平均障碍物碰撞率，优于代表性的模仿学习基线，并提高了杂乱农业场景中的泛化能力。
英文摘要：
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.
