arXiv cs.AI· Ying Yuan, David Held, Jitendra Malik, Haozhi Qi·· 3 天前AI 评分26
FOMO-Dex：面向接触密集操作任务的统一柔顺控制方法
Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation
AI 导读
研究者提出 Force-Modulated Dexterity（FOMO-Dex），一种面向接触密集操作的统一柔顺控制框架，在任务几何与接触结构定义的坐标中表达标称动作，沿选定方向施加力驱动的运动修正。
正文 · 原文
Abstract:Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control of motion and contact forces. Operators can specify motion, but struggle to reproduce the subtle force adjustments of human manipulation without feeling the robot's contacts. We introduce Force-Modulated Dexterity (FOMO-Dex), a unified compliance formulation for contact-rich manipulation. FOMO-Dex expresses nominal actions in coordinates defined by task geometry and contact structure. It applies force-driven motion corrections along selected directions while leaving the remaining command components unchanged. Across card picking, paper cutting, screwdriving, and nut-and-bolt assembly, FOMO-Dex reduces excessive contact forces compared with stiff control. A user study shows that operators collect more successful demonstration data per hour after brief practice. For policy learning, imitating the corrected actions improves card-picking success over imitating raw operator commands. Policies trained on FOMO-Dex demonstrations also achieve more rotation in nut-and-bolt manipulation and screwdriving than those trained on demonstrations collected with the stiff controller. Videos are shown on this https URL .
| Comments: | Project page: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17161 [cs.RO] |
| (or arXiv:2610.17161v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17161 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ying Yuan [view email]
[v1]
Mon, 12 Oct 2026 17:37:23 UTC (4,648 KB)
来源：arXiv cs.AI · arxiv.org