We synthesize shared control protocols subject to probabilistic temporal
logic specifications. More 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 (MDP) that
represents the shared control scenario. Using inverse reinforcement learning,
we obtain an abstraction of the human’s behavior and decisions. We use
randomized strategies to account for randomness in human’s decisions, caused by
factors such as 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.

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