Customized Handling of Unintended Interface Operation In Assistive Robots

We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate modifications on unintended behavior. Existing methods typically treat the human and control interface as a black box and assume the measured user input is noise-free, and use this signal to infer task-level human intent. We recognize that the signal measured through the interface is masked by the physical limitations of the user and the interface they are required to use. With this key insight, we model the human's physical interaction with a control interface during robot teleoperation, and distinguish between interface-level intended and measured physical actions explicitly. By reasoning over the unobserved intentions using model-based inference techniques, our assistive system provides customized modifications on a user's issued commands. We validate our algorithm both in simulation and with a 10-person human subject study in which we evaluate the performance of the proposed assistance paradigms. Our results show that the assistance paradigms helped to significantly reduce task completion time, number of mode switches, cognitive workload, and user frustration, and improve overall user satisfaction.