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ObjectiveThis study aims to improve workers’ postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. BackgroundHuman-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. MethodsThe proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers’ continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers’ CAP score by adjusting the positions and orientations of the robot end effector. ResultsIn an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. ConclusionThe proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. ApplicationThe proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.
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Mutual reinforcement learning with robot trainers
The researchers in this study have developed a novel approach using mutual reinforcement learning (MRL) where both the robot and human act as empathetic individuals who function as reinforcement learning agents for each other to achieve a particular task over continuous communication and feedback. This shared model not only has a collective impact but improves human cognition and helps in building a successful human-robot relationship. In our current work, we compared our learned reinforcement model with a baseline non-reinforcement and random approach in a robotics domain to identify the significance and impact of MRL. MRL contributed to improved skill transfer, and the robot was able successfully to predict which reinforcement behaviors would be most valuable to its human partners.
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- Award ID(s):
- 1659645
- PAR ID:
- 10173021
- Date Published:
- Journal Name:
- Proceedings of the 2019 ACM Conference on Human-Robot Interaction
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HRI.2019.8673284
