Incremental Temporal Logic Synthesis of Control Policies for Robots Interacting with Dynamic Agents

TL;DR

This paper presents an incremental method for synthesizing control policies for robots interacting with dynamic agents, using temporal logic, Markov models, and an anytime algorithm to improve computational efficiency and satisfaction probability.

Contribution

It introduces an incremental synthesis approach that manages complexity by gradually incorporating environment agents, enhancing scalability in probabilistic control policy synthesis.

Findings

Algorithm improves satisfaction probability over time.

Partial agent incorporation reduces computational load.

Method applicable to stochastic and deterministic models.

Abstract

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.