FoAR：面向富接触机器人操作的力感知反应式策略
FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
AI总结：
针对富接触机器人操作中视觉策略缺乏力反馈的问题，提出力感知反应式策略FoAR，结合高频力/力矩传感与视觉输入，通过未来接触预测器引导的多模态融合机制动态调整数据使用，在多种任务中显著优于基线并保持抗扰动性。
摘要：
富接触任务因接触动力学复杂且需精确控制，给机器人操作策略带来了重大挑战。基于视觉的策略通常缺乏力/力矩等关键接触反馈模态，难以胜任此类任务。为此，我们提出FoAR，一种力感知反应式策略，结合高频力/力矩传感与视觉输入以提升富接触操作性能。FoAR基于RISE策略构建，引入由未来接触预测器引导的多模态特征融合机制，实现非接触与接触阶段间力/力矩数据使用的动态调整。其反应式控制策略也使FoAR能通过简单的位置控制准确完成富接触任务。实验结果表明，FoAR在多种具挑战性的富接触任务上显著优于所有基线，并在意外动态扰动下保持稳健性能。项目网站：https://tonyfang.net/FoAR/
英文摘要：
Contact-rich tasks present significant challenges for robotic manipulation policies due to the complex dynamics of contact and the need for precise control. Vision-based policies often struggle with the skill required for such tasks, as they typically lack critical contact feedback modalities like force/torque information. To address this issue, we propose FoAR, a force-aware reactive policy that combines high-frequency force/torque sensing with visual inputs to enhance the performance in contact-rich manipulation. Built upon the RISE policy, FoAR incorporates a multimodal feature fusion mechanism guided by a future contact predictor, enabling dynamic adjustment of force/torque data usage between non-contact and contact phases. Its reactive control strategy also allows FoAR to accomplish contact-rich tasks accurately through simple position control. Experimental results demonstrate that FoAR significantly outperforms all baselines across various challenging contact-rich tasks while maintaining robust performance under unexpected dynamic disturbances. Project website: https://tonyfang.net/FoAR/
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。
