Learning Adaptive Navigation Strategies for Resource-constrained Systems

The majority of navigation algorithms for mobile robots assume that the robots possess enough computational or memory resources to carry out the necessary calculations. Especially small and lightweight devices, however, are resource-constrained and have only restricted capabilities. In this paper, we present a reinforcement learning approach for mobile robots that considers the imposed constraints on their sensing capabilities and computational resources, so that they can reliably and efficiently fulfill their navigation tasks. Our technique learns a policy that optimally trades off the speed of the robot and the uncertainty in the observations imposed by its movements. It furthermore enables the robot to learn an efficient landmark selection strategy to compactly model the environment. We describe extensive simulated and real-world experiments carried out with both wheeled and humanoid robots which demonstrate that our learned navigation policies significantly outperform strategies using advanced and manually optimized heuristics.