An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots
This work addresses the challenge of sample complexity in reinforcement learning for K-12 education and human-in-the-loop systems, but it is incremental as it builds on existing interactive RL concepts with AR enhancements.
The paper tackles the problem of hidden robot states in interactive reinforcement learning by proposing an Augmented Reality system to reveal these states to human teachers, aiming to improve human-robot collaboration.
Interactive reinforcement learning, where humans actively assist during an agent's learning process, has the promise to alleviate the sample complexity challenges of practical algorithms. However, the inner workings and state of the robot are typically hidden from the teacher when humans provide feedback. To create a common ground between the human and the learning robot, in this paper, we propose an Augmented Reality (AR) system that reveals the hidden state of the learning to the human users. This paper describes our system's design and implementation and concludes with a discussion on two directions for future work which we are pursuing: 1) use of our system in AI education activities at the K-12 level; and 2) development of a framework for an AR-based human-in-the-loop reinforcement learning, where the human teacher can see sensory and cognitive representations of the robot overlaid in the real world.