Incremental Temporal Logic Synthesis of Control Policies for Robots Interacting with Dynamic Agents
This work addresses computational efficiency in robot control synthesis for dynamic environments, but it is incremental as it adapts existing probabilistic verification methods.
The paper tackles the synthesis of control policies for robots interacting with dynamic agents from temporal logic specifications, proposing an incremental approach to mitigate state explosion, resulting in an anytime algorithm that increases satisfaction probability as it progresses.
We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modeled by a Markov chain whereas the robot is modeled by a finite transition system (in the deterministic case) or Markov decision process (in the stochastic case). Existing results in probabilistic verification are adapted to solve the synthesis problem. To partially address the state explosion issue, we propose an incremental approach where only a small subset of environment agents is incorporated in the synthesis procedure initially and more agents are successively added until we hit the constraints on computational resources. Our algorithm runs in an anytime fashion where the probability that the robot satisfies its specification increases as the algorithm progresses.