# A Human-Sensitive Controller: Adapting to Human Musculoskeletal Disorder-Related Constraints via Reinforcement Learning

arXiv: https://arxiv.org/abs/2504.10102 · HTML: https://mlsanity.com/paper/2504.10102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 14 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot locomotion and manipulation](https://mlsanity.com/deeprl/rl-for-robot-locomotion-and-manipulation/)

## Builds on (most cited first, 2 of 2)

- [Human-Robot Gym: Benchmarking Reinforcement Learning in Human-Robot Collaboration](https://mlsanity.com/paper/2310.06208/) (2023)
- [Maximising Coefficiency of Human-Robot Handovers through Reinforcement Learning](https://mlsanity.com/paper/2306.07205/) (2023)

## Abstract (arXiv)

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.