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Simultaneous model identification and task satisfaction in the presence of temporal logic constraints

Chinchali, Sandeep P. and Livingston, Scott C. and Pavone, Marco and Burdick, Joel W. (2016) Simultaneous model identification and task satisfaction in the presence of temporal logic constraints. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 3682-3689. ISBN 978-1-4673-8026-3.

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Recent proliferation of cyber-physical systems, ranging from autonomous cars to nuclear hazard inspection robots, has exposed several challenging research problems on automated fault detection and recovery. This paper considers how recently developed formal synthesis and model verification techniques may be used to automatically generate information-seeking trajectories for anomaly detection. In particular, we consider the problem of how a robot could select its actions so as to maximally disambiguate between different model hypotheses that govern the environment it operates in or its interaction with other agents whose prime motivation is a priori unknown. The identification problem is posed as selection of the most likely model from a set of candidates, where each candidate is an adversarial Markov decision process (MDP) together with a linear temporal logic (LTL) formula that constrains robot-environment interaction. An adversarial MDP is an MDP in which transitions depend on both a (controlled) robot action and an (uncontrolled) adversary action. States are labeled, thus allowing interpretation of satisfaction of LTL formulae, which have a special form admitting satisfaction decisions in bounded time. An example where a robotic car must discern whether neighboring vehicles are following its trajectory for a surveillance operation is used to demonstrate our approach.

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Additional Information:© 2016 IEEE. This work was partially supported by United Technologies Corporation and IBM, through the industrial cyberphysical systems (iCyPhy) consortium, and by NASA under the Space Technology Research Grants Program, Grant NNX12AQ43G. S.P Chinchali is supported by a Stanford Graduate Fellowship and National Science Foundation Fellowship. The authors thank Richard M. Murray for comments on an early draft.
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United Technologies CorporationUNSPECIFIED
Stanford Graduate FellowshipUNSPECIFIED
Record Number:CaltechAUTHORS:20170111-152319486
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73454
Deposited By: Kristin Buxton
Deposited On:20 Jan 2017 00:36
Last Modified:11 Nov 2021 05:16

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