A musculoskeletal model-based Assistance-As-Needed paradigm for assistive robotics

A Musculoskeletal Model-based Assistance-As-Needed Paradigm for Assistive Robotics Robotic systems which operate collaboratively with their human operators to provide assistance are becoming reality, and many different paradigms for administering this assistance have been developed. A promising paradigm is Assistance-As-Needed, which aims to provide physical assistance specific to the individual requirements of the operator. This requires that the needs of the operator be determined, which is challenging as they depend on both the task being performed, and the capability of the operator to perform it. Current solutions use performance-based methods which critique the operator from observations obtained during tasks, and then adapt assistance based on how they performed. This approach has shown success in applications such as robotic rehabilitation. However, empirical performance-based methods have inherent limitations, primarily due to the numerous observations required before the operator’s assistance needs can be determined. The ideal Assistance-As-Needed paradigm should be able to determine the operator’s assistance requirements without prior observations, and with respect to arbitrary tasks. This thesis presents a novel Assistance-As-Needed paradigm using models to estimate the assistance needs of the human operator. An optimisation model is developed which utilises a publicly available musculoskeletal model representing the human upper limb to estimate their strength, which is compared to the strength required by the task being performed to gauge their assistance requirements. An advantage of this model-based