让机器人更懂力反馈，提升复杂抓取成功率
FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
- 融合六维力/力矩信号，动态调节动作生成
- 预测未来动作与受力变化，显式建模接触演化
- 实时修正动作，适合高精度接触任务
力信号为接触密集型机器人操作提供关键交互线索。然而，现有方法多将力信号作为附加观测，未充分挖掘其在建模未来交互动态或执行时反馈校正中的作用。本文提出FAWAM，一种三层次融合力信息的世界动作模型：首先编码历史6轴力/力矩信号以调制动作生成；其次联合预测未来动作与末端执行器力/力矩，显式建模接触演化；进一步引入残差校正模块，利用预测力矩轨迹作为实时参考，在执行阶段基于真实力反馈在线优化动作。真实世界实验表明，相较于仅依赖视觉的基线，FAWAM平均成功率提升36.25%；相比现有力感知基线，提升21.25%，验证了该力感知框架在鲁棒接触密集型操作中的有效性。
原文摘要 · Abstract (English)
Force signals provide critical interaction cues for contact-rich robotic manipulation. However, existing methods mostly use force as an additional observation modality, without fully exploiting its role in modeling future interaction dynamics or guiding execution-time feedback correction. In this paper, we propose FAWAM, a force-aware world action model that incorporates force information at three levels: perception, prediction, and closed-loop execution. FAWAM first encodes historical 6-axis force/torque signals to modulate action generation, then jointly predicts future actions and end-effector wrenches to explicitly model contact evolution. It further introduces a residual correction module that uses the predicted wrench trajectory as an execution-time reference to refine actions online based on real-time force feedback. Real-world experiments across multiple contact-rich tasks show that FAWAM improves the average success rate by 36.25% over vision-only baselines and 21.25% over existing force-aware baselines, demonstrating the effectiveness of our force-aware framework for robust contact-rich manipulation.
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