Towards an Adaptive Social Game-Playing Robot: An Offline Reinforcement Learning-Based Framework

HRI research increasingly demands robots that go beyond task execution to respond meaningfully to user emotions. This is especially needed when supporting students with learning difficulties in game-based learning scenarios. Here, the objective of these robots is to train users with game-playing skills, and this requires robots to get input about users' interests and engagement. In this paper, we present a system for an adaptive social game-playing robot. However, creating such an agent through online RL requires extensive real-world training data and potentially be uncomfortable for users. To address this, we investigate offline RL as a safe and efficient alternative. We introduce a system architecture that integrates multimodal emotion recognition and adaptive robotic responses. We also evaluate the performance of various offline RL algorithms using a dataset collected from a real-world human-robot game-playing scenario. Our results indicate that BCQ and DDQN offer the greatest robustness to hyperparameter variations, whereas CQL is the most effective at mitigating overestimation bias. Through this research, we aim to inform the selection and design of reliable offline RL policies for real-world social robotics. Ultimately, this work provides a foundational step toward creating socially intelligent agents that can learn complex and emotion-adaptive behaviors entirely from offline datasets, ensuring both human comfort and practical scalability.