Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
This addresses scalable coordination for multi-robot systems under uncertainty, representing an incremental advance with domain-specific impact.
The paper tackles multi-robot planning in continuous spaces with partial observability by extending Dec-POMDPs to Dec-POSMDPs for scalability and asynchronous decision-making, resulting in a method that provides high-quality solutions for large-scale problems like package delivery.
The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (Dec-POMDPs) are general models for multi-robot coordination problems, but representing and solving Dec-POMDPs is often intractable for large problems. To allow for a high-level representation that is natural for multi-robot problems and scalable to large discrete and continuous problems, this paper extends the Dec-POMDP model to the decentralized partially observable semi-Markov decision process (Dec-POSMDP). The Dec-POSMDP formulation allows asynchronous decision-making by the robots, which is crucial in multi-robot domains. We also present an algorithm for solving this Dec-POSMDP which is much more scalable than previous methods since it can incorporate closed-loop belief space macro-actions in planning. These macro-actions are automatically constructed to produce robust solutions. The proposed method's performance is evaluated on a complex multi-robot package delivery problem under uncertainty, showing that our approach can naturally represent multi-robot problems and provide high-quality solutions for large-scale problems.