Generation of Context-Dependent Policies for Robot Rescue Decision-Making in Multi-Robot Teams

Abstract

We propose a scalable, parallelizable policy synthesis framework intended for a robot presented with the decision of exploration or rescue, given some time-varying, stochastic mission conditions, referred to as context. We demonstrate the feasibility of such a solution using physics-based simulations to synthesize a policy in a computationally-efficient manner and exhibit superior performance with regards to the minimization of probability of mission failure when compared to two feasible baseline approaches. Furthermore, we present preliminary results that suggest our approach is robust to errors in the state estimation used to build mission context, which further supports the notion of real-world applicability.