DEXOP：用於機器人轉移靈巧人類操作技能的裝置

摘要

我們引入了perioperation這一機器人數據收集範式，它通過傳感化並記錄人類操作來最大化數據向真實機器人的可遷移性。我們在DEXOP中實現了這一範式，這是一種被動式手部外骨骼，旨在增強人類在自然環境中收集豐富感官（視覺+觸覺）數據的能力，以應對多種靈巧操作任務。DEXOP將人類手指與機器人手指機械連接，為用戶提供直接接觸反饋（通過本體感覺），並將人手姿態鏡像到被動機器人手上，從而最大化演示技能向機器人的轉移。相比遙操作，力反饋和姿態鏡像使任務演示對人類來說更加自然，提高了速度和準確性。我們在多種需要豐富接觸的靈巧任務中評估了DEXOP，展示了其大規模收集高質量演示數據的能力。使用DEXOP數據學習的策略，在單位數據收集時間內顯著提升了任務表現，使DEXOP成為推進機器人靈巧性的有力工具。我們的項目頁面位於https://dex-op.github.io。

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.

DEXOP：用於機器人轉移靈巧人類操作技能的裝置

DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation

摘要

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