Emergence of bipedal walking through body/environment interactions

In biological regulatory systems, all computations result from spatial and temporal combination of simple and homogeneous computational media. This computational scheme realize the adaptability to unpredictable environmental changes, which is one of the most salient features of biological regulations. To investigate the learning process behind this computational scheme, we propose a learning method that embodies the features of biological systems, termed tacit learning. We have constructed a controller based on the notion of tacit learning and applied it to the control of the 36DOF humanoid robot to create the bipedal walking adapted to the environment. Experiments on walking showed a remarkably high adaptation capability of tacit learning in terms of gait generations, power consumption and robustness.