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• W3067857708 abstract "We synthesize shared control protocols subject to probabilistic temporal logic specifications. Specifically, we develop a framework in which a human and an autonomy protocol can issue commands to carry out a certain task. We blend these commands into a joint input to a robot. We model the interaction between the human and the robot as a Markov decision process representing the shared control scenario. Using inverse reinforcement learning, we obtain an abstraction of the human's behavior. We use randomized strategies to account for randomness in human's decisions, caused by factors such as the complexity of the task specifications or imperfect interfaces. We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic. Additionally, the resulting strategies generate behavior as similar to the behavior induced by the human's commands as possible. We solve the underlying problem efficiently using quasiconvex programming. Case studies involving autonomous wheelchair navigation and unmanned aerial vehicle mission planning showcase the applicability of our approach." @default.
• W3067857708 created "2020-08-24" @default.
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• W3067857708 date "2021-07-01" @default.
• W3067857708 modified "2023-10-17" @default.
• W3067857708 title "Synthesis of Provably Correct Autonomy Protocols for Shared Control" @default.
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• W3067857708 doi "https://doi.org/10.1109/tac.2020.3018029" @default.
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