FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
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Abstract (arXiv)
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
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 10 Jun 2026.
Builds on
31 of 48 references.
References · 31
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19 more
- 2024Re-Mix: Optimizing Data Mixtures for Large Scale Imitation Learningcited by 47
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Citing papers per year: 2026: 2.
2 papers.