人間に配慮した制御器：筋骨格系障害に関連する制約への強化学習による適応
A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning
強化学習（Q学習とDQN）を用いて協働ロボットの人間に配慮した制御戦略を開発し、筋骨格系障害歴のある作業者の痛みリスクをゼロに保ちつつ作業時間を短縮できることを示した。
著者: Vitor Martins, Sara M. Cerqueira, Mercedes Balcells, Elazer R Edelman, Cristina P. Santos
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Work-Related Musculoskeletal Disorders continue to be a major challenge in industrial environments, leading to reduced workforce participation, increased healthcare costs, and long-term disability. This study introduces a human-sensitive robotic system aimed at reintegrating individuals with a history of musculoskeletal disorders into standard job roles, while simultaneously optimizing ergonomic conditions for the broader workforce. This research leverages reinforcement learning (RL) to develop a human-aware control strategy for collaborative robots, focusing on optimizing ergonomic conditions and preventing pain during task execution. Two RL approaches, Q-Learning and Deep Q-Network (DQN), were implemented and tested to personalize control strategies based on individual user characteristics. Although experimental results revealed a simulation-to-real gap, a fine-tuning phase successfully adapted the policies to real-world conditions. DQN outperformed Q-Learning by completing tasks faster while maintaining zero pain risk and safe ergonomic levels, achieving on average 38% shorter task completion times across all tested anthropometries. The structured testing protocol confirmed the system's adaptability to diverse human anthropometries, underscoring the potential of RL-driven cobots to enable safer, more inclusive workplaces.