Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia

Abstract

Autonomous navigation of mobile robot in unknown environment has attracted much attention of scholars over the past decades, and many bio-inspired heuristic navigation models have been presented, such as the cerebellum and basal ganglia models. Pervious cerebellum and basal ganglia model, however, treats them as parallel and independent system, without interaction between them, or combines their function together, but with only unidirectional communication between them. Based on the cognition and developmental mechanism of the biology, this paper proposes a novel navigation model, which uses the motivated developmental network (MDN) to mimic the supervised learning of the cerebellum and the reinforcement learning based on the radial basis function neural network (RBFNN) to simulate the reward-based learning of the basal ganglia, and integrates them together to construct a hybrid complex cognition model, to navigate a mobile robot in unknown environment. During the environment exploration, for the unexplored places, the artificial agent uses the cerebellum model to choose action, instead of the ∊-greedy method, to accelerate the learning convergence speed of the basal ganglia. For the explored places, it directly uses the basal ganglia based on the RBFNN to explore, then update and perfect the knowledge base of the cerebellum, which enable the cerebellum to achieve better decision in the following exploration. Hence, it realizes not only the two way communication between the cerebellum and basal ganglia, but also the co-development of them. Experimental results show that this model can enable the agent to autonomously development its intelligence through the hybrid learning. As far as we know, this is the first work to realize the direct communication between the basal ganglia and the cerebellum.