Stability analysis of tacit learning based on environmental signal accumulation

Abstract

Tacit learning is the novel learning scheme based on the principle of biological control to create the appropriate behaviors adapted to the environment. Signal accumulation is the key factor for tacit learning in the process of behavior adaptation. To clarify the role of the signal accumulation in the learning process, we analyzed it dividing into the two processes depending on the control speed. The fast process is used for the behavior control and the slow process is used for the behavior adaptation to the environment. We developed the continuous-time controller for tacit learning with the integrators and showed that the signal accumulation can estimate a part of the robot model through the interactions between the robot body and the environment. This capability of tacit learning is useful to control a plant where the modeling errors and model changes are the critical problems for the stable controls. As the prominent example of the control of such plant, we experimentally verified that tacit learning can create the bipedal walking gait that pushes the ground by the support leg at the moment of losing contact with the ground.