Probabilistic robot planning under model uncertainty : an active learning approach
While recent POMDP techniques have been successfully applied to the problem of robot control under uncertainty, they typically assume a known (and stationary) model of the environment. In this paper, we study the problem of finding an optimal policy for controlling a robot in a partially observable domain, where the model is not perfectly known, and may change over time. We present an algorithm called MEDUSA which incrementally learns a POMDP model using oracle queries, while still optimizing a reward function. We demonstrate effectiveness of the approach for realistic robot planning scenarios, with minimal a priori knowledge of the model.