arXiv:2606.12406v1 Announce Type: cross 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
The continuous drive for more capable and accessible robotics, coupled with advancements in machine learning, makes this a natural progression for improving robot arm functionality.
This development significantly lowers the barrier to entry for advanced robotic manipulation, expanding the potential applications of robot arms to tasks requiring fine force control without specialized, expensive hardware.
Robot arms, particularly lower-cost models, can now perform contact-rich manipulation tasks with force feedback previously only possible with costly dedicated sensors, increasing their utility and accessibility.
- · Manufacturers of commodity robot arms
- · Logistics and manufacturing sectors
- · AI/ML robotics developers
- · Small and medium enterprises adopting automation
- · Incumbent manufacturers of high-cost force sensors
- · Companies reliant on manual labor for precision tasks
Wider adoption of robotics in new industrial and service applications requiring dexterity and force sensitivity.
Increased competition among robot manufacturers as advanced capabilities become democratized, driving down costs and further accelerating adoption.
Enhanced human-robot collaboration possibilities as robots become more responsive to physical interaction, fostering new forms of work and interfaces.
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Read at arXiv cs.LG