多様な教示インターフェース：より柔軟なロボット教示データ収集に向けて
Versatile Demonstration Interface: Toward More Flexible Robot Demonstration Collection
協働ロボットに取り付けるアタッチメント型インターフェースを提案し、視覚・力覚・状態追跡を組み合わせて3種類の教示を簡単に収集できるようにした。製造現場の専門家によるユーザー調査で実用性を検証した。
著者: Michael Hagenow, Dimosthenis Kontogiorgos, Yanwei Wang, Julie Shah
分類: cs.RO
原文アブストラクト
Previous methods for Learning from Demonstration leverage several approaches for a human to teach motions to a robot, including teleoperation, kinesthetic teaching, and natural demonstrations. However, little previous work has explored more general interfaces that allow for multiple demonstration types. Given the varied preferences of human demonstrators and task characteristics, a flexible tool that enables multiple demonstration types could be crucial for broader robot skill training. In this work, we propose Versatile Demonstration Interface (VDI), an attachment for collaborative robots that simplifies the collection of three common types of demonstrations. Designed for flexible deployment in industrial settings, our tool requires no additional instrumentation of the environment. Our prototype interface captures human demonstrations through a combination of vision, force sensing, and state tracking (e.g., through the robot proprioception or AprilTag tracking). Through a user study where we deployed our prototype VDI at a local manufacturing innovation center with manufacturing experts, we demonstrated VDI in representative industrial tasks. Interactions from our study highlight the practical value of VDI's varied demonstration types, expose a range of industrial use cases for VDI, and provide insights for future tool design.
