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

翻译：接触丰富的操作任务需要力敏感性，但许多机械臂因成本高昂而缺乏专用力传感器。我们提出神经外力矩估计方法（NEXT），这是一种无需专用力传感器即可估计外力矩的数据驱动方法。NEXT仅需10分钟自由运动数据即可在1分钟内完成训练，却能达到与专用关节力矩传感器相当的估计精度。NEXT使低成本机械臂具备力反馈遥操作能力，并通过力引导重采样训练（FIRST）提升策略学习效果——该方法在行为克隆过程中对预接触和接触阶段进行过采样。在五项长时域任务中，FIRST的任务进度指标相较先前力感知策略提升超过17%。综合而言，NEXT与FIRST使现有机电臂无需额外传感硬件即可实现力感知遥操作与策略学习。视频结果与代码详见 https://jasonjzliu.com/factr2