可适配多用户的手部外骨骼，直接用摄像头拍到的数据训练机器人
DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning
- 手部外骨骼设计兼顾舒适性与跨用户适应性
- 支持140~217mm不同手长，减少个体适配时间
- 硬件级外观对齐，直接用原始视觉数据训练模型
提升灵巧机器人学习效率的关键瓶颈在于跨用户高质量示范数据的获取。现有可穿戴设备常在舒适性与运动精度之间妥协，且示范与部署间的身体形态差异需额外视觉处理才能用于策略训练。本文提出DexEXO，一种以穿戴舒适性为核心的灵巧手外骨骼，从硬件层面实现视觉外观、接触几何与运动学的一致性。其采用姿态容错拇指结构和基于滑块的指节接口，经解析建模可适配140~217mm手长范围，显著降低个体化调节需求，支持大规模跨用户数据采集。被动式手部结构与部署机器人视觉一致，可直接使用腕部搭载的RGB相机原始观测进行策略训练。用户实验表明其舒适性和易用性优于现有系统。仅依赖视觉对齐观测，即可训练出性能优异的扩散策略，大幅简化端到端流程。结果表明，优先考虑穿戴性与硬件级具身对齐，可在不牺牲任务表现的前提下同时突破人与算法的双重瓶颈。
原文摘要 · Abstract (English)
Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while embodiment mismatch between demonstration and deployment requires visual post-processing before policy training. We present DexEXO, a wearability-first hand exoskeleton that aligns visual appearance, contact geometry, and kinematics at the hardware level. DexEXO features a pose-tolerant thumb mechanism and a slider-based finger interface analytically modeled to support hand lengths from 140~mm to 217~mm, reducing operator-specific fitting and enabling scalable cross-operator data collection. A passive hand visually matches the deployed robot, allowing direct policy training from raw wrist-mounted RGB observations. User studies demonstrate improved comfort and usability compared to prior wearable systems. Using visually aligned observations alone, we train diffusion policies that achieve competitive performance while substantially simplifying the end-to-end pipeline. These results show that prioritizing wearability and hardware-level embodiment alignment reduces both human and algorithmic bottlenecks without sacrificing task performance. Project Page: https://dexexo-research.github.io/
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