Extended Reality System for Robotic Learning from Human Demonstration

TL;DR

This paper introduces RADER, an extended reality system enabling safe and versatile robot learning from human demonstrations, matching physical robot performance without safety risks.

Contribution

The paper presents RADER, a novel extended reality interface for robot learning from demonstration, expanding safety and interaction options compared to traditional methods.

Findings

RADER achieves comparable results to physical demonstrations.

Extended reality enables safe, versatile robot training.

The system integrates with existing learning algorithms.

Abstract

Many real-world tasks are intuitive for a human to perform, but difficult to encode algorithmically when utilizing a robot to perform the tasks. In these scenarios, robotic systems can benefit from expert demonstrations to learn how to perform each task. In many settings, it may be difficult or unsafe to use a physical robot to provide these demonstrations, for example, considering cooking tasks such as slicing with a knife. Extended reality provides a natural setting for demonstrating robotic trajectories while bypassing safety concerns and providing a broader range of interaction modalities. We propose the Robot Action Demonstration in Extended Reality (RADER) system, a generic extended reality interface for learning from demonstration. We additionally present its application to an existing state-of-the-art learning from demonstration approach and show comparable results between demonstrations given on a physical robot and those given using our extended reality system.