Using stochastic optimal feedback control and computational motor control to design personalized and adaptive human robot interfaces

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Human-computer interfaces and human-robot interfaces are everywhere, ranging from smart-phones to intelligent cars to exoskeletons to myoelectric interfaces. As these interfaces become more sophisticated, it becomes harder for engineers to match the tunable parameters of the interface to the complex goals, rewards, and dynamics of humans-particularly for stochastic (noise-corrupted) interfaces. Recent advances in human-movement theory have produced simple causal mappings between input parameters (like motion and force) and the things people care about (like accuracy, effort, and responsiveness). Existing human-machine interface designs typically only focus on one aspect (such as maximizing accuracy) and do not leverage our understanding of these dynamical mappings to maximize the rewards that end-users value. My long-term objective is to flip the paradigm of human-machine interface design, such that engineers use these dynamical mappings to form a bridge between what end-users care about and the parameters that engineers can tune. My short-term objectives are: 1.Develop models that are causal, efficient, human-like, and incorporate human-machine interface uncertainty caused by signal corruption. We will merge advances across branches of the field of computational motor control using numerically efficient methods. 2.Refine computational motor control experiments. Our preliminary work suggests that some conventional methods result in bias due to subconscious user adaptation, which can be mitigated by refining experimental techniques and using random-process analytical techniques. 3.Develop a platform that enables personalized tuning of human-machine interfaces. We will develop numerically efficient techniques that find optimal mappings for given rewards, allowing the user to safely adjust parameters in real-time by articulating their relative reward preferences. 4.Extend our approach to tasks with long-term goals, such as coaching. Similar to how chess algorithms look far enough ahead to win a game, we will use recent advances that implicitly factor in potential learning gains when choosing training actions. This approach will enable coaches to choose training actions that are tailored to the long-term dynamics and implicit goals of individuals. Each of our aims will be validated across experiments comparing our solutions to conventional techniques, using appropriate statistical design and considering EDI factors. This broad program leverages human movement science to inform the optimal design of devices that interact with humans. It represents a highly original paradigm shift in how we think about design that will contribute to groundbreaking advances in several fields including smartphones, human-robot interfaces, and assistive/augmenting devices such as exoskeletons, and lead to concrete technologies that address the critical socio-economic need to harmonize the sophistication of devices with the complex goals of individuals.