[Paper] FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
Source: arXiv - 2606.12406v1
Overview
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 Contributions
This paper presents research in the following areas:
- cs.RO
- cs.AI
- cs.LG
- eess.SY
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Steven Oh
- Jason Jingzhou Liu
- Tony Tao
- Philip Han
- Kenneth Shaw
- Satoshi Funabashi
- Ruslan Salakhutdinov
- Deepak Pathak
Paper Information
- arXiv ID: 2606.12406v1
- Categories: cs.RO, cs.AI, cs.LG, eess.SY
- Published: June 10, 2026
- PDF: Download PDF