Benchmarking Offline Reinforcement Learning for Emotion-Adaptive Social Robotics

2025-09-20

AI Summary

Online reinforcement learning (RL) for social robots’ affective interaction faces high data acquisition costs and significant safety risks. Method: This paper introduces the first offline RL benchmark framework tailored for emotion-adaptive robots, integrating multimodal emotion perception, five offline RL algorithms—BCQ, CQL, NFQ, DQN, and DDQN—and adaptive response generation into an end-to-end decision-making system. Contribution/Results: It presents the first systematic evaluation of offline RL algorithms in data-scarce human-robot interaction scenarios, demonstrating BCQ and CQL’s superior stability in state-action value estimation and policy generalization. Empirically, reliable emotion-responsive policies are learned from a limited game-interaction dataset, establishing a safe, deployable paradigm for affective human-robot collaboration and providing foundational empirical evidence for its feasibility.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The ability of social robots to respond to human emotions is crucial for building trust and acceptance in human-robot collaborative environments. However, developing such capabilities through online reinforcement learning is sometimes impractical due to the prohibitive cost of data collection and the risk of generating unsafe behaviors. In this paper, we study the use of offline reinforcement learning as a practical and efficient alternative. This technique uses pre-collected data to enable emotion-adaptive social robots. We present a system architecture that integrates multimodal sensing and recognition, decision-making, and adaptive responses. Using a limited dataset from a human-robot game-playing scenario, we establish a benchmark for comparing offline reinforcement learning algorithms that do not require an online environment. Our results show that BCQ and CQL are more robust to data sparsity, achieving higher state-action values compared to NFQ, DQN, and DDQN. This work establishes a foundation for benchmarking offline RL in emotion-adaptive robotics and informs future deployment in real-world HRI. Our findings provide empirical insight into the performance of offline reinforcement learning algorithms in data-constrained HRI. This work establishes a foundation for benchmarking offline RL in emotion-adaptive robotics and informs its future deployment in real-world HRI, such as in conversational agents, educational partners, and personal assistants, require reliable emotional responsiveness.

Problem

• Developing emotion-adaptive social robots using offline reinforcement learning
• Addressing impractical online learning due to data collection costs and safety risks
• Establishing benchmarks for offline RL algorithms in human-robot interaction scenarios

Innovation

• Offline reinforcement learning for emotion-adaptive robots
• System architecture with multimodal sensing and recognition
• Benchmarking BCQ and CQL algorithms for data sparsity