NRI: INT: Individualized Co-Robotics

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This project explores new ways to meet the needs of individual patients and users with customized active artificial limbs, motorized ankle and knee supports, and other forms of assistive robots. The effectiveness of such devices depends on accommodating person-to-person differences, such as in body types, walking gaits, and impairments. A preliminary study investigated the degree to which an individually adapted sequence of control patterns for a powered ankle exoskeleton -- modified as the subject used the device -- could reduce the metabolic cost of walking. This outcome showed that optimizing the assistive control pattern separately for each subject greatly increased the average benefits compared to the non-optimized device, and outperformed any previously documented approach. This project builds upon those preliminary results to find better and more robust customization methods for a wide range of assistive technology in diverse circumstances. This approach to "human-in-the-loop" optimization of human-collaborative robots could have a wide impact on improved assistive and therapeutic devices and environments that reduce the risk of falls in older adults; help mitigate developmental disorders in children; and assist workers, soldiers, and first responders with physical tasks. Ultimately these customization methods could improve performance of everyday items like shoes and exercise equipment.The scientific goal of the project is to find optimization approaches to robustly improve co-robotic interaction with users, facilitating physical collaboration. Scaling up to support a variety of devices, joints, behaviors, tasks, environments, and users is also a key goal. The project will explore how long a set of optimized parameters stay valid, how to optimize device behavior during actual use without reducing task performance or device acceptance, and how to assist a variety of behaviors where the user determines when and how a behavior is engaged. In terms of scope, the project will initially focus on how the parameterization of robot behavior, the choice of optimization algorithm, and enhancement of human learning by interaction with a co-robot can all play an important role in achieving this goal. In terms of methods, the project will use both laboratory-based exoskeleton emulators as well as portable exoskeleton devices that can go outdoors to develop and test ideas and approaches, as well as psychophysical studies that will improve understanding of how muscle is controlled. Co-optimization involves both the co-robot and the user optimizing their interface policies simultaneously, and thus presenting time-varying targets for each other's optimization. The project will improve theoretical understanding of co-optimization in the context of physical co-robotics. The project will also add new molecular phenomenon to current muscle models, to provide a physiological basis and understanding of human-robot physical interaction. The intellectual significance of this work includes better understanding of how humans work, how to most effectively assist humans, and how to best customize assistance for an individual. A long term goal is to build a library of customized interaction policies for an individual performing a variety of tasks, and tune the library online as the user does desired tasks.