Motivated developmental learning for flexible behavioral decision-making of mobile robots in unknown environment

The flexible behavioral decision-making of mobile robots in unknown environment is the premise of completing various tasks. The current robot behavior decision methods are not flexible in facing the dynamic environments, and it is difficult for the robots to obtain continuous and stable learning ability. In our previous work, the supervised learning of cerebellum and reinforcement learning of basal ganglia is integrated to achieve flexible behavior decision of mobile robot in dynamic environment. However, the proposed algorithm has limited adaptability to the dynamic environment. Based on the previous work, a more biologically meaningful curiosity index is designed to replace the original vigilance index, by simulating the dynamic switch of locus coeruleus activity between tonic mode and phasic mode, the dynamic adaptive adjustment of environment exploration and exploitation of the mobile robot is realized. At the same time, an adaptive adjustment factor changing with the external environment is designed to realize the flexible behavioral decision of mobile robot in dynamic environment based on the supervised learning of the cerebellum, and reinforcement learning of basal ganglia, make the robot obtain stable learning ability. Experimental results in dynamic environment and real environment verify the effectiveness of the proposed algorithm. © 2023 South China University of Technology. All rights reserved.