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arXiv · 1806.02425

Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance

Aleksandra Kalinowska·Kathleen Fitzsimons·Julius Dewald·Todd D Murphey
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Abstract

We propose a novel criterion for evaluating user input for human-robot interfaces for known tasks. We use the mode insertion gradient (MIG)---a tool from hybrid control theory---as a filtering criterion that instantaneously assesses the impact of user actions on a dynamic system over a time window into the future. As a result, the filter is permissive to many chosen strategies, minimally engaging, and skill-sensitive---qualities desired when evaluating human actions. Through a human study with 28 healthy volunteers, we show that the criterion exhibits a low, but significant, negative correlation between skill level, as estimated from task-specific measures in unassisted trials, and the rate of controller intervention during assistance. Moreover, a MIG-based filter can be utilized to create a shared control scheme for training or assistance. In the human study, we observe a substantial training effect when using a MIG-based filter to perform cart-pendulum inversion, particularly when comparing improvement via the RMS error measure. Using simulation of a controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe that the MIG criterion could be used for assistance to guarantee either task completion or safety of a joint human-robot system, while maintaining the system's flexibility with respect to user-chosen strategies.

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Aleksandra Kalinowska, Kathleen Fitzsimons, Julius Dewald, Todd D Murphey. 2018-06-06. Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance. https://doi.org/10.15607/rss.2018.xiv.046

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