Synthesis of Provably Correct Autonomy Protocols
for Shared Control

Murat Cubuktepe, Nils Jansen, Mohammed Alsiekh, Ufuk Topcu

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Abstract—We develop algorithms to 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 the robot. We model the interaction between the human
and the robot as a Markov decision process that represents
the shared control scenario. Additionally, we use randomized
strategies in a Markov decision process to incorporate potential
randomness in human’s decisions, which is caused by factors such
as complexity of the task specifications and imperfect interfaces.
Using inverse reinforcement learning, we obtain an abstraction
of the human’s behavior as a so-called randomized strategy. We
design the autonomy protocol to ensure that the robot behavior—
resulting from the blending of the commands—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 formulate the underlying problem as a quasiconvex
optimization problem, which is solved by checking feasibility
of a number of linear programming problems. We show the
applicability of the approach through case studies involving
autonomous wheelchair navigation and unmanned aerial vehicle
mission planning.


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