Augmented Reality Demonstrations for Scalable Robot Imitation Learning
Yue Yang Bryce Ikeda Gedas Bertasius Daniel Szafir
Robot Imitation Learning (IL) is a widely used method for training robots to
perform manipulation tasks that involve mimicking human demonstrations to
acquire skills. However, its practicality has been limited due to its
requirement that users be trained in operating real robot arms to provide
demonstrations. This paper presents an innovative solution: an Augmented
Reality (AR)-assisted framework for demonstration collection, empowering
non-roboticist users to produce demonstrations for robot IL using devices like
the HoloLens 2. Our framework facilitates scalable and diverse demonstration
collection for real-world tasks. We validate our approach with experiments on
three classical robotics tasks: reach, push, and pick-and-place. The real robot
performs each task successfully while replaying demonstrations collected via
AR.