Decentralized Decision-Making Under Uncertainty for Multi-Robot Teams

Automatically generating solutions to general multi-robot coordination problems with communication limitations is challenging, but crucial in many domains. As one way to address this problem, we describe a probabilistic framework for synthesizing control policies for general multi-robot systems based on decentralized partially observable Markov decision processes with macro-actions (MacDec-POMDPs). MacDecPOMDPs are a general model of decision-making where a team of robots cooperates to optimize a shared objective in the presence of uncertainty. MacDec-POMDPs also consider communication limitations, so execution is decentralized. We describe how, in contrast to most existing methods that are specialized to a particular problem class, we can synthesize control policies that exploit whatever opportunities for coordination are present in the problem, while balancing uncertainty, sensor information, and information about other robots.