Enabling Robots to Infer how End-Users Teach and Learn through Human-Robot Interaction
This addresses the challenge of enabling robots to adapt to diverse human teaching and learning styles in HRI, though it is incremental as it builds on existing Bayesian methods.
The paper tackles the problem of robots interpreting human interaction strategies during human-robot interaction by proposing a personalized approach that infers individual user strategies using Bayesian inference, showing it outperforms standard fixed-strategy methods in simulations.
During human-robot interaction (HRI), we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we can leverage interactions, where the human and robot observe each other's behavior. However, it is not always clear how the human and robot should interpret these actions: a given interaction might mean several different things. Within today's state-of-the-art, the robot assigns a single interaction strategy to the human, and learns from or teaches the human according to this fixed strategy. Instead, we here recognize that different users interact in different ways, and so one size does not fit all. Therefore, we argue that the robot should maintain a distribution over the possible human interaction strategies, and then infer how each individual end-user interacts during the task. We formally define learning and teaching when the robot is uncertain about the human's interaction strategy, and derive solutions to both problems using Bayesian inference. In examples and a benchmark simulation, we show that our personalized approach outperforms standard methods that maintain a fixed interaction strategy.