arXiv 2602.073882026-02-07cs.RO

面向长时程机器人操作的多模态动作消歧的轨迹聚焦扩散策略

Trace-Focused Diffusion Policy for Multi-Modal Action Disambiguation in Long-Horizon Robotic Manipulation

MARS Lab, Nanyang Technological University（MARS实验室，南洋理工大学）

Yuxuan Hu, Xiangyu Chen, Chuhao Zhou, Yuxi Liu, Gen Li, Jindou Jia, Jianfei Yang

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AI总结：

TF-DP通过轨迹聚焦解决长时程机器人操作中的多模态动作模糊问题，提升时间一致性和鲁棒性，实验结果优于普通扩散策略。

摘要：

基于生成模型的策略在通过学习演示中的动作分布进行模仿式机器人操作中表现出了强大的性能。然而，在长时程任务中，视觉上相似的观察经常在执行阶段中反复出现，但需要不同的动作，这导致当策略仅基于瞬时观察进行条件时产生模糊的预测，称为多模态动作模糊（MA2）。为了解决这一挑战，我们提出了轨迹聚焦扩散策略（TF-DP），一种简单而有效的基于扩散的框架，它明确地将动作生成条件化于机器人的执行历史。TF-DP将历史运动表示为显式的执行轨迹，并将其投影到视觉观察空间中，在当前观察不足时提供阶段意识的上下文。此外，诱导的轨迹聚焦场强调与历史运动相关的任务相关区域，提高了对背景视觉干扰的鲁棒性。我们评估了TF-DP在具有明显多模态动作模糊和视觉杂乱条件的真实机器人操作任务上。实验结果表明，TF-DP提高了时间一致性和鲁棒性，在具有多模态动作模糊的任务中比普通扩散策略提高了80.56个百分点，在视觉干扰下提高了86.11个百分点，同时保持了推理效率，仅增加了6.4%的运行时间。这些结果表明，执行轨迹条件化提供了一种可扩展且原则性的方法，用于在单个策略内实现稳健的长时程机器人操作。

英文摘要：

Generative model-based policies have shown strong performance in imitation-based robotic manipulation by learning action distributions from demonstrations. However, in long-horizon tasks, visually similar observations often recur across execution stages while requiring distinct actions, which leads to ambiguous predictions when policies are conditioned only on instantaneous observations, termed multi-modal action ambiguity (MA2). To address this challenge, we propose the Trace-Focused Diffusion Policy (TF-DP), a simple yet effective diffusion-based framework that explicitly conditions action generation on the robot's execution history. TF-DP represents historical motion as an explicit execution trace and projects it into the visual observation space, providing stage-aware context when current observations alone are insufficient. In addition, the induced trace-focused field emphasizes task-relevant regions associated with historical motion, improving robustness to background visual disturbances. We evaluate TF-DP on real-world robotic manipulation tasks exhibiting pronounced multi-modal action ambiguity and visually cluttered conditions. Experimental results show that TF-DP improves temporal consistency and robustness, outperforming the vanilla diffusion policy by 80.56 percent on tasks with multi-modal action ambiguity and by 86.11 percent under visual disturbances, while maintaining inference efficiency with only a 6.4 percent runtime increase. These results demonstrate that execution-trace conditioning offers a scalable and principled approach for robust long-horizon robotic manipulation within a single policy.

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