Customized Handling of Unintended Interface Operation in Assistive Robots
This addresses the issue of user errors and inefficiencies in assistive robot teleoperation, offering a domain-specific incremental improvement.
The paper tackles the problem of unintended interface operations during robot teleoperation by developing an assistance system that infers human intentions and provides customized corrections, resulting in significant reductions in task completion time, mode switches, cognitive workload, and user frustration, along with improved satisfaction in a 10-person study.
We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior. We model the human's physical interaction with a control interface during robot teleoperation and distinguish between intended and measured physical actions explicitly. By reasoning over the unobserved intentions using model-based inference techniques, our assistive system provides customized corrections on a user's issued commands. We validate our algorithm 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.