学习何时视觉何时触觉：适应性视觉-扭矩融合用于接触感知操控
Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation
AI总结：
本文提出适应性视觉-扭矩融合策略，通过比较不同融合方法提升机器人操控性能，实验显示方法在成功率上优于基线14%。
摘要：
基于视觉的策略在机器人操控中表现出色，因其视觉观测的可得性和丰富性。然而，在接触丰富且力敏感的任务中，纯视觉感知不足，因为力/扭矩（F/T）信号提供了关于接触动态、对齐和交互质量的关键信息。尽管已提出多种整合视觉和F/T信号的策略，包括辅助预测目标、专家混合架构和接触感知门控机制，但缺乏这些方法的比较。本文在扩散基础操控策略中比较了不同F/T-视觉整合策略，并提出了一种适应性整合策略，在非接触阶段忽略F/T信号，在接触阶段适应性利用视觉和扭矩信息。实验结果表明，我们的方法在成功率上比最强基线高出14%，突显了接触感知多模态融合在机器人操控中的重要性。
英文摘要：
Vision-based policies have achieved a good performance in robotic manipulation due to the accessibility and richness of visual observations. However, purely visual sensing becomes insufficient in contact-rich and force-sensitive tasks where force/torque (F/T) signals provide critical information about contact dynamics, alignment, and interaction quality. Although various strategies have been proposed to integrate vision and F/T signals, including auxiliary prediction objectives, mixture-of-experts architectures, and contact-aware gating mechanisms, a comparison of these approaches remains lacking. In this work, we provide a controlled comparison of different F/T-vision integration strategies within diffusion-based manipulation policies. In addition, we propose an adaptive integration strategy that ignores F/T signals during non-contact phases while adaptively leveraging both vision and torque information during contact. Experimental results demonstrate that our method outperforms the strongest baseline by 14% in success rate, highlighting the importance of contact-aware multimodal fusion for robotic manipulation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
