Physically assistive robots and exoskeletons have great potential to help humans with a wide variety of collaborative 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 inform the design of real-time assistance strategies for human-robot collaboration problems.

Many assistive robotic devices have been developed to augment or assist human locomotion. Despite advancements in design and control algorithms, this task remains challenging. Human walking strategies are unique and complex, and assistance strategies based on the dynamics of unassisted locomotion typically offer only modest reductions to the metabolic cost of walking. Recently, human-in-the-loop (HIL) methodologies have been used to identify subject-specific assistive strategies, which offer significant improvements to energy savings. However, current implementations suffer from long measurement times, necessitating the use of low-dimensional control parameterizations, and possibly requiring multiday collection protocols to avoid subject fatigue. We present a HIL methodology, which optimizes the assistive torques provided by a powered hip exoskeleton. Using musculoskeletal modeling, we are able to evaluate simulated metabolic rate online. We applied our methodology to identify assistive torque profiles for seven subjects walking on a treadmill, and found greater reductions to metabolic cost when compared to generic or off-the-shelf controllers. In a secondary investigation, we directly compare simulated and measured metabolic rate for three subjects experiencing a range of assistance levels. The time investment required to identify assistance strategies via our protocol is significantly lower when compared to existing protocols relying on calorimetry. In the future, frameworks such as these could be used to enable shorter HIL protocols or exploit more complex control parameterizations for greater energy savings.

Currently for balance recovery, humans outperform humanoid robots which use hand-designed controllers in terms of the diverse actions. This study aims to close this gap by finding core control principles that are shared across ankle, hip, toe and stepping strategies by formulating experiments to test human balance recoveries and define criteria to quantify the strategy in use. To reveal fundamental principles of balance strategies, our study shows that a minimum jerk controller can accurately replicate comparable human behaviour at the Centre of Mass level. Therefore, we formulate a general Model-Predictive Control (MPC) framework to produce recovery motions in any system, including legged machines, where the framework parameters are tuned for time-optimal performance in robotic systems.