无需力传感器，让廉价机械臂感知外力并提升操作性能
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
- 用1分钟训练数据估算关节外部扭矩，替代昂贵力传感器
- 在5个长任务中使策略学习任务进度提升超17%
- 适合想低成本升级机械臂感知能力的研究者和工程师
接触密集型操作需要力感知，但许多机器人手臂因成本高而缺乏专用力传感器。我们提出神经外部扭矩估计（NEXT），一种无需任何专用力传感器即可估算外部关节扭矩的数据驱动方法。NEXT仅需10分钟自由运动数据，在1分钟内完成训练，其估计精度可媲美专用关节扭矩传感器。NEXT实现了低成本机械臂的力反馈遥操作，并通过力感知重采样训练（FIRST）改进策略学习，该方法在行为克隆过程中对接触前与接触段进行上采样。在五个长时序任务中，FIRST相比先前力感知策略的任务进度提升超过17%。总体而言，NEXT与FIRST将力感知遥操作和策略学习引入市售机器人，无需额外传感硬件。视频结果与代码见 https://jasonjzliu.com/factr2
原文摘要 · Abstract (English)
Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost. We present Neural External Torque Estimation (NEXT), a data-driven method that estimates external joint torques without needing any dedicated force sensors. NEXT trains in 1 minute from only 10 minutes of free-motion data, yet achieves estimates comparable to dedicated joint-torque sensors. NEXT enables force-feedback teleoperation on low-cost arms and improves policy learning through Force-Informed Re-Sampling Training (FIRST), which up-samples pre-contact and contact segments during behavior cloning. Across five long-horizon tasks, FIRST outperforms prior force-aware policies by over 17% in task progress. Together, NEXT and FIRST bring force-aware teleoperation and policy learning to off-the-shelf robots without additional sensing hardware. Video results and code are available at https://jasonjzliu.com/factr2
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