Controlling mobile robots with complex articulated parts and hence many degrees of freedom generates high cognitive load on the operator, especially under demanding conditions such as in Urban Search & Rescue missions. We propose a solution based on reinforcement learning in order to accommodate the robot morphology automatically to the terrain and the obstacles it traverses. In this paper, we concentrate on the crucial issue of predicting rewards from incomplete or missing data. For this purpose we exploit the Gaussian processes as a predictor combined with decision trees. We demonstrate our achievements in a series of experiments on real data.

In this paper we introduce the concept of Adaptive Traversability (AT), which we define as means of autonomous motion control adapting the robot morphology - configuration of articulated parts and their compliance - to traverse unknown complex terrain with obstacles in an optimal way. We verify this concept by proposing a reinforcement learning based AT algorithm for mobile robots operating in such conditions. We demonstrate the functionality by training the AT algorithm under lab conditions on simple EUR-pallet obstacles and then testing it successfully on natural obstacles in a forest. For quantitative evaluation we define a metrics based on comparison with expert operator. Exploiting the proposed AT algorithm significantly decreases the cognitive load of the operator.