Finger force sensing improves box manipulation reliability and safety
Proprioceptive Finger Force Estimation for Reliable Box Manipulation
Robotics
Summary
Gripping boxes safely is hard because you need to hold them firmly without breaking them. The authors created a way to estimate how much force each finger applies just by using the robot's own movement sensors, without extra force sensors. Their method predicts finger forces more accurately and helps the robot adjust its grip in real-time. This leads to better success at picking and placing boxes without damage.
What this means in practice
- •For robotic system engineers: Implement per-finger force estimation without dedicated sensors to improve handling of delicate objects during robot pick-and-place tasks.
- •For prosthetics developers: Enhance finger-level force feedback in prosthetic hands by estimating contact forces from proprioceptive signals alone.
Authors
Junha Cha, Chanwoo Kim, Kisang Park, Yeoncheol Jang, Doyoung Kim, Seungmin Kim, Kyungjae Lee, Sungjoon Choi
Abstract
Reliable box manipulation requires successful placement without damage, which depends on maintaining sufficient contact force without excessive loading. We hypothesize that per-finger force feedback improves manipulation reliability relative to open-loop finger commands. To this end, we present Finger External-Force Estimation from Proprioceptive SignaLs (FEEL), an uncertainty-aware residual method that estimates per-finger external force magnitude from proprioceptive histories. Our method combines a calibrated virtual-spring estimate with a residual distribution predicted by a Mixture Density Network (MDN) and requires no dedicated contact sensors during deployment. The estimated force is used for continuous per-finger adaptation, while predictive uncertainty is evaluated as an empirical signal of ambiguity in proprioceptive force estimation. Across ten fingers and contact forces spanning 0-20 N, our method achieves a mean RMSE of 0.251 N, reducing the error by 92.8% relative to the virtual spring and by 9.4% relative to an end-to-end MDN. In box pick-and-place, force adaptation maintains task success at 10/10 and increases both damage-free outcomes and reliability from 7/10 to 10/10. The project page is available at https://finger-force-estimation.pages.dev/