外观
论文arXiv 2610.1716116 天前
力调制灵巧性：面向富接触操作的统一柔顺性
原文链接：Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation（arXiv 2610.17161 · 发布日期：2026-10-12）
Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation
针对遥操作难以复现细微接触力的问题，FOMO-Dex以统一柔顺形式进行力驱动修正，并在多种富接触操作中验证。
核心要点
- 动机是灵巧操作需协调运动与接触力，而操作者缺少机器人接触反馈。
- 方法将名义动作表达在由任务几何和接触结构定义的坐标中。
- FOMO-Dex沿选定方向施加力驱动运动修正，其他命令分量保持不变。
- 实验显示其相较刚性控制降低过大接触力，并提升示教采集和策略学习表现。
验证范围与局限：验证范围为卡片拾取、剪纸、拧螺丝和螺母螺栓装配等任务。
英文摘要 (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 https://yingyuan0414.github.io/fomo-dex/ .
作者：Ying Yuan, David Held, Jitendra Malik, Haozhi Qi
来源
本页事实对应的公开页面，以原文为准。