Synthesis of Provably Correct Autonomy Protocols for Shared Control
This work addresses the challenge of ensuring safe and effective human-robot interaction in shared control systems, such as assistive devices and autonomous vehicles, though it is incremental in building on existing MDP and inverse reinforcement learning methods.
The paper tackles the problem of synthesizing shared control protocols for human-robot collaboration that satisfy probabilistic temporal logic specifications, achieving this by blending human and autonomy commands into joint inputs and ensuring safety and performance with efficient quasiconvex programming, as demonstrated in case studies like autonomous wheelchair navigation and UAV mission planning.
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.