用可穿戴外骨骼让真人操作数据高效转移给机器人，提升机械手灵巧性。
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
- 通过人机同步外骨骼捕捉视觉与触觉数据，实现自然示范。
- 相比遥控操作，单位数据收集时间性能提升显著，适合复杂任务。
- 适合研究机器人灵巧操作与人机协作的团队使用。
我们提出一种名为perioperation的机器人数据采集范式，通过传感和记录人类在自然环境中进行多样灵巧操作时的多模态数据（视觉+触觉），以最大化数据向真实机器人的迁移能力。我们实现了该范式于DEXOP——一种被动式手部外骨骼，其将人类手指与机器人手指机械连接，提供直接本体感觉反馈，并镜像人类手部姿态至被动机器人手，从而最大化技能迁移效果。力反馈与姿态同步使示范过程更自然，提升了速度与准确率。我们在多种接触密集型灵巧操作任务上评估了DEXOP，验证了其规模化采集高质量示范数据的能力。基于DEXOP数据训练的策略，在单位数据收集时间内显著优于遥控操作，证明DEXOP是提升机器人灵巧性的强大工具。项目主页：https://dex-op.github.io。
原文摘要 · Abstract (English)
We introduce perioperation, a paradigm for robotic data collection that sensorizes and records human manipulation while maximizing the transferability of the data to real robots. We implement this paradigm in DEXOP, a passive hand exoskeleton designed to maximize human ability to collect rich sensory (vision + tactile) data for diverse dexterous manipulation tasks in natural environments. DEXOP mechanically connects human fingers to robot fingers, providing users with direct contact feedback (via proprioception) and mirrors the human hand pose to the passive robot hand to maximize the transfer of demonstrated skills to the robot. The force feedback and pose mirroring make task demonstrations more natural for humans compared to teleoperation, increasing both speed and accuracy. We evaluate DEXOP across a range of dexterous, contact-rich tasks, demonstrating its ability to collect high-quality demonstration data at scale. Policies learned with DEXOP data significantly improve task performance per unit time of data collection compared to teleoperation, making DEXOP a powerful tool for advancing robot dexterity. Our project page is at https://dex-op.github.io.
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