Versatile Demonstration Interface: Toward More Flexible Robot Demonstration Collection

TL;DR

This paper introduces a versatile demonstration interface (VDI) for robots that supports multiple demonstration types, enhancing flexibility and practicality in industrial robot skill training without additional environmental instrumentation.

Contribution

The paper presents a novel, flexible robot demonstration tool capable of capturing various demonstration types using vision, force sensing, and state tracking, suitable for industrial settings.

Findings

VDI effectively captures multiple demonstration types.

User study shows VDI's practical utility in industrial tasks.

Insights for future robot demonstration tool design.

Abstract

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.