DexEXO：一种以可穿戴性优先的灵活外骨骼，用于操作者无关的演示和学习
DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning
AI总结：
DexEXO通过硬件层面的外观、接触几何和运动学对齐，提升了可穿戴性，实现了跨操作者数据收集和简化端到端管道。
摘要：
在扩展灵活机器人学习方面，现有方法受限于在多样操作者中收集高质量演示的难度。现有可穿戴接口通常为运动学保真度牺牲了舒适性和跨用户适应性，而演示与部署之间的身体匹配需要视觉后处理才能进行策略训练。我们提出了DexEXO，一种以可穿戴性优先的手外骨骼，其在硬件层面对齐了视觉外观、接触几何和运动学。DexEXO具有耐姿势拇指机制和基于滑块的手指接口，经过分析建模以支持手长从140~mm到217~mm，减少了操作者特定的适配需求，实现了可扩展的跨操作者数据收集。被动手在视觉上与部署的机器人匹配，允许从直接的腕部RGB观测进行策略训练。用户研究显示，与先前的可穿戴系统相比，DexEXO在舒适性和可用性方面有所改进。使用仅视觉对齐的观测，我们训练了扩散策略，实现了具有竞争力的性能，同时显著简化了端到端管道。这些结果表明，优先考虑可穿戴性和硬件层面的外观对齐，可以减少人类和算法的瓶颈，而不会牺牲任务性能。项目页面：https://dexexo-research.github.io/
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
