This project proposes a formalism for customizable shared control, that enables users to customize the way they share control with intelligent assistive devices based on their abilities and preferences. In our formalism, the system arbitrates between the user input and the autonomous policy prediction, based on the confidence it has in the policy’s prediction and in the user’s ability to perform the task.
In this work, we proposed a mathematical framework which formalizes user-driven customization of shared autonomy in assistive robotics as a nonlinear optimization problem. The key insight was to allow the end-user, rather than relying on standard optimization techniques, to perform the optimization procedure, thereby allowing us to leave the exact nature of the cost function indeterminate. That is, customization of how control is shared directly in the hands of the end-user. An exploratory study with spinal cord injured and uninjured subjects allowed subjects to verbally customize the function which governed how control was shared. Results show all subjects were able to converge to an assistance paradigm, suggesting the existence of optimal solutions. Notably, the amount of assistance was not always optimized for task performance. Instead, some subjects favored retaining more control during the execution over better task performance.
Link to paper.