DEXOP：一种用于灵巧人类操作机器人迁移的设备
DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation
AI总结：
DEXOP是一种被动手部外骨骼，通过机械连接和姿态镜像实现人类操作数据的高效收集与迁移，显著提升机器人灵巧任务性能。
摘要：
我们提出了perioperation，一种用于机器人数据收集的范式，该范式对人类的操作进行传感和记录，同时最大化数据向真实机器人的可迁移性。我们在DEXOP中实现了这一范式，DEXOP是一种被动式手部外骨骼，旨在最大化人类在自然环境中收集多样化灵巧操作任务的丰富感官（视觉+触觉）数据的能力。DEXOP通过机械方式将人类手指与机器人手指连接，为用户提供直接接触反馈（通过本体感觉），并将人类手部姿态镜像到被动机器人手上，以最大化演示技能向机器人的迁移。与遥操作相比，力反馈和姿态镜像使任务演示对人类来说更加自然，提高了速度和准确性。我们在各种灵巧、接触密集的任务中评估了DEXOP，展示了其大规模收集高质量演示数据的能力。与遥操作相比，使用DEXOP数据学习的策略显著提高了每单位数据收集时间的任务性能，使DEXOP成为提升机器人灵巧性的强大工具。我们的项目页面位于https://dex-op.github.io。
英文摘要：
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.
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。
