Active visual sensing and collaboration on mobile robots using hierarchical POMDPs

A key challenge to widespread deployment of mobile robots in the real-world is the ability to robustly and autonomously sense the environment and collaborate with teammates. Real-world domains are characterized by partial observability, non-deterministic action outcomes and unforeseen changes, making autonomous sensing and collaboration a formidable challenge. This paper poses visionbased sensing, information processing and collaboration as an instance of probabilistic planning using partially observable Markov decision processes. Reliable, efficient and autonomous operation is achieved using a hierarchical decomposition that includes: (a) convolutional policies to exploit the local symmetry of high-level visual search; (b) adaptive observation functions, policy re-weighting, automatic belief propagation and online updates of the domain map for autonomous adaptation to domain changes; and (c) a probabilistic strategy for a team of robots to robustly share beliefs. All algorithms are evaluated in simulation and on physical robots localizing target objects in dynamic indoor domains.