FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw +3 more
6/10/2026
cs.ROcs.AIcs.LG

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Abstract

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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Cite this paper

@article{oh2026factr,
  title  = {FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning},
  author = {Steven Oh and Jason Jingzhou Liu and Tony Tao and Philip Han and Kenneth Shaw and Satoshi Funabashi and Ruslan Salakhutdinov and Deepak Pathak},
  year   = {2026},
  eprint = {2606.12406},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2606.12406v1}
}

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