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.

[1]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[2]  H. Okano,et al.  Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations. , 2006, Journal of theoretical biology.

[3]  Hidenori Kimura,et al.  Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion , 2008 .

[4]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[5]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[6]  Y. Kuniyoshi,et al.  Emergence and development of embodied cognition: a constructivist approach using robots. , 2007, Progress in brain research.

[7]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  M. Paradiso,et al.  Neuroscience: Exploring the Brain , 1996 .

[9]  H. Markram,et al.  Redistribution of synaptic efficacy between neocortical pyramidal neurons , 1996, Nature.

[10]  Russ Tedrake,et al.  Efficient Bipedal Robots Based on Passive-Dynamic Walkers , 2005, Science.

[11]  J. Monod,et al.  Genetic regulatory mechanisms in the synthesis of proteins. , 1961, Journal of Molecular Biology.

[12]  E. Reed.  The Ecological Approach to Visual Perception , 1989 .

[13]  H. Pontzer,et al.  Chimpanzee locomotor energetics and the origin of human bipedalism , 2007, Proceedings of the National Academy of Sciences.

[14]  H. Okano,et al.  Mathematical description of gene regulatory units. , 2006, Biophysical journal.

[15]  A. Hodgkin,et al.  Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo , 1952, The Journal of physiology.

[16]  W. Precht.  The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.

[17]  Rodney A. Brooks,et al.  A Robust Layered Control Syste For A Mobile Robot , 2022 .

[18]  M. Kawato,et al.  A hierarchical neural-network model for control and learning of voluntary movement , 2004, Biological Cybernetics.

[19]  H. Kimura,et al.  Mathematical classification of regulatory logics for compound environmental changes. , 2008, Journal of theoretical biology.

[20]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[21]  Hidenori Kimura,et al.  Biomimetic Approach to Tacit Learning Based on Compound Control , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.