Adaptability of Tacit Learning in Bipedal Locomotion

The capability of adapting to unknown environmental situations is one of the most salient features of biological regulations. This capability is ascribed to the learning mechanisms of biological regulatory systems that are totally different from the current artificial machine-learning paradigm. We consider that all computations in biological regulatory systems result from the spatial and temporal integration of simple and homogeneous computational media such as the activities of neurons in brain and protein-protein interactions in intracellular regulations. Adaptation is the outcome of the local activities of the distributed computational media. To investigate the learning mechanism behind this computational scheme, we proposed a learning method that embodies the features of biological systems, termed tacit learning. In this paper, we elaborate this notion further and applied it to bipedal locomotion of a 36DOF humanoid robot in order to discuss the adaptation capability of tacit learning comparing with that of conventional control architectures and that of human beings. Experiments on walking revealed a remarkably high adaptation capability of tacit learning in terms of gait generation, power consumption and robustness.