Daniel Gordon, Andreas Christou, Theodoros Stouraitis, Michael Gienger, Sethu Vijayakumar, "Learning Personalised Human Sit-to-Stand Motion Strategies via Inverse Musculoskeletal Optimal Control ", IEEE International Conference on Robotics and Automation, 2023.

Physically assistive robots and exoskeletons have great potential to help humans with a wide variety of collabora- tive tasks. However, a challenging aspect of the control of such devices is to accurately model or predict human behaviour, which can be highly individual and personalised. In this work, we implement a framework for learning subject-specific models of underlying human motion strategies using inverse musculoskeletal optimal control. We apply this framework to a specific motion task: the sit-to-stand transition. By collecting sit- to-stand data from 4 subjects with and without perturbations, we show that humans modulate their sit-to-stand strategy in the presence of instability, and learn the corresponding models of these strategies. In the future, the personalised motion strategies resulting from this framework could be used to predict individual human responses to candidate assistance strategies for human-robot collaboration problems.