融合力觉与视觉，让机械臂更精准完成高接触任务
FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation
- 用力觉反馈+视觉信息动态融合，智能调节接触阶段控制策略
- 在多种高接触任务中表现优于所有基线模型，抗干扰能力强
- 适合需要精细力控的工业抓取、装配等复杂操作场景
高接触任务对机器人操控带来巨大挑战，源于接触动力学复杂且需精确控制。基于视觉的策略常因缺乏力/扭矩等关键触觉反馈而表现不佳。为此，我们提出FoAR——一种力觉感知的反应式策略，将高频力/扭矩传感与视觉输入结合，显著提升接触丰富任务的表现。基于RISE策略，FoAR引入由未来接触预测引导的多模态特征融合机制，可在非接触与接触阶段动态调整力觉数据使用。其反应式控制策略使系统仅通过简单位置控制即可准确完成高接触任务。实验表明，FoAR在多种挑战性接触任务中显著超越所有基线，在意外动态扰动下仍保持鲁棒性能。
原文摘要 · Abstract (English)
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/
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