做机器人操作研究的团队终于有了低成本力感知方案——NEXT仅需10分钟数据就能替代昂贵传感器，FIRST让行为克隆在接触任务中提升17%进度，建议做遥操作或灵巧操作的开发者直接试。
本文提出NEXT方法，通过数据驱动的方式仅用10分钟自由运动数据训练1分钟，即可估计机械臂外部关节力矩，无需专用力传感器。结合FIRST重采样训练策略，在行为克隆中提升接触任务表现，五个长时任务中任务进度提升超17%。该方法让低成本机械臂也能实现力反馈遥操作和策略学习，无需额外硬件。代码和视频已开源。
FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
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