Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10102 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909579020861440 |
|---|---|
| author | Martins, Vitor Cerqueira, Sara M. Balcells, Mercedes Edelman, Elazer R Santos, Cristina P. |
| author_facet | Martins, Vitor Cerqueira, Sara M. Balcells, Mercedes Edelman, Elazer R Santos, Cristina P. |
| contents | 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 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. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10102 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Human-Sensitive Controller: Adapting to Human Ergonomics and Physical Constraints via Reinforcement Learning Martins, Vitor Cerqueira, Sara M. Balcells, Mercedes Edelman, Elazer R Santos, Cristina P. Robotics Systems and Control 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 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. 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. |
| title | A Human-Sensitive Controller: Adapting to Human Ergonomics and Physical Constraints via Reinforcement Learning |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2504.10102 |