固有感覚信号による指外力推定で信頼性の高い箱操作を実現
Proprioceptive Finger Force Estimation for Reliable Box Manipulation
固有感覚履歴から指ごとの外力を推定する不確実性考慮型の残差手法FEELを提案し、接触センサなしで箱のピック&プレースの成功率と無損傷率を向上させた。
著者: Junha Cha, Chanwoo Kim, Kisang Park, Yeoncheol Jang, Doyoung Kim, Seungmin Kim, Kyungjae Lee, Sungjoon Choi
分類: cs.RO
原文アブストラクト
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/