Extended Reality System for Robotic Learning from Human Demonstration

Many tasks are intuitive for humans but difficult to encode algorithmically when utilizing a robot. Robotic systems often benefit from learning from expert demonstrations, wherein operators move the robot along trajectories. Often, using a physical robot to provide these demonstrations may be difficult or unsafe. Extended reality provides a natural setting for demonstrating trajectories while bypassing safety concerns or modifying existing solution trajectories. We propose the Robot Action Demonstration in Extended Reality (RADER) system for learning from demonstration approaches. We additionally present its application to a state-of-the-art approach and show comparable results between using a physical robot and our system.