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Proceedings Paper

Dynamic inverse models in human-cyber-physical systems
Author(s): Ryan M. Robinson; Dexter R. R. Scobee; Samuel A. Burden; S. Shankar Sastry
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Paper Abstract

Human interaction with the physical world is increasingly mediated by automation. This interaction is characterized by dynamic coupling between robotic (i.e. cyber) and neuromechanical (i.e. human) decision-making agents. Guaranteeing performance of such human-cyber-physical systems will require predictive mathematical models of this dynamic coupling. Toward this end, we propose a rapprochement between robotics and neuromechanics premised on the existence of internal forward and inverse models in the human agent. We hypothesize that, in tele-robotic applications of interest, a human operator learns to invert automation dynamics, directly translating from desired task to required control input. By formulating the model inversion problem in the context of a tracking task for a nonlinear control system in control-a_ne form, we derive criteria for exponential tracking and show that the resulting dynamic inverse model generally renders a portion of the physical system state (i.e., the internal dynamics) unobservable from the human operator's perspective. Under stability conditions, we show that the human can achieve exponential tracking without formulating an estimate of the system's state so long as they possess an accurate model of the system's dynamics. These theoretical results are illustrated using a planar quadrotor example. We then demonstrate that the automation can intervene to improve performance of the tracking task by solving an optimal control problem. Performance is guaranteed to improve under the assumption that the human learns and inverts the dynamic model of the altered system. We conclude with a discussion of practical limitations that may hinder exact dynamic model inversion.

Paper Details

Date Published: 25 May 2016
PDF: 20 pages
Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 98361X (25 May 2016); doi: 10.1117/12.2223176
Show Author Affiliations
Ryan M. Robinson, U.S. Army Research Lab. (United States)
Dexter R. R. Scobee, Univ. of California, Berkeley (United States)
Samuel A. Burden, Univ. of Washington (United States)
S. Shankar Sastry, Univ. of California, Berkeley (United States)


Published in SPIE Proceedings Vol. 9836:
Micro- and Nanotechnology Sensors, Systems, and Applications VIII
Thomas George; Achyut K. Dutta; M. Saif Islam, Editor(s)

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