Enhancing Robot Assistive Behaviour with Reinforcement Learning and Theory of Mind
This addresses the challenge of improving robot assistive behavior for human users, though it is incremental as it builds on existing reinforcement learning and ToM concepts with a comparative study.
The study tackled the problem of enhancing human-robot collaboration by integrating Theory of Mind (ToM) abilities into robots, finding that participants interacting with a ToM-equipped robot performed better, accepted assistance more often, and perceived the robot's adaptability more positively in a real-world user study with 56 participants.
The adaptation to users' preferences and the ability to infer and interpret humans' beliefs and intents, which is known as the Theory of Mind (ToM), are two crucial aspects for achieving effective human-robot collaboration. Despite its importance, very few studies have investigated the impact of adaptive robots with ToM abilities. In this work, we present an exploratory comparative study to investigate how social robots equipped with ToM abilities impact users' performance and perception. We design a two-layer architecture. The Q-learning agent on the first layer learns the robot's higher-level behaviour. On the second layer, a heuristic-based ToM infers the user's intended strategy and is responsible for implementing the robot's assistance, as well as providing the motivation behind its choice. We conducted a user study in a real-world setting, involving 56 participants who interacted with either an adaptive robot capable of ToM, or with a robot lacking such abilities. Our findings suggest that participants in the ToM condition performed better, accepted the robot's assistance more often, and perceived its ability to adapt, predict and recognise their intents to a higher degree. Our preliminary insights could inform future research and pave the way for designing more complex computation architectures for adaptive behaviour with ToM capabilities.