Cognitive and action control for goal-directed reaching in a humanoid robot

Abstract

Human-level performance in robotic systems remains elusive. Therefore, an important complementary approach to developing intelligent systems is to use design principles of the brain. One major design element is the combination of multiple behavioral control systems. Although hierarchical architectures have been used in robotics, they have not closely mimicked those of the primate brain (including humans). We are developing a model of cognitive and action control in the human brain that captures key findings from the cognitive neuroscience literature. Here, we focus on the classic detour problem that requires reaching around barriers. We demonstrate model performance in both simulation and with a humanoid robot and show with the simulations that all levels in the model contribute to successful reaching, provided that the agent's environment poses problems of varying complexity.