A Reflexive Neural Network for Dynamic Biped Walking Control

Biped walking remains a difficult problem, and robot models can greatly facilitate our understanding of the underlying biomechanical principles as well as their neuronal control. The goal of this study is to specifically demonstrate that stable biped walking can be achieved by combining the physical properties of the walking robot with a small, reflex-based neuronal network governed mainly by local sensor signals. Building on earlier work (Taga, 1995; Cruse, Kindermann, Schumm, Dean, & Schmitz, 1998), this study shows that human-like gaits emerge without specific position or trajectory control and that the walker is able to compensate small disturbances through its own dynamical properties. The reflexive controller used here has the following characteristics, which are different from earlier approaches: (1) Control is mainly local. Hence, it uses only two signals (anterior extreme angle and ground contact), which operate at the interjoint level. All other signals operate only at single joints. (2) Neither position control nor trajectory tracking control is used. Instead, the approximate nature of the local reflexes on each joint allows the robot mechanics itself (e. g., its passive dynamics) to contribute substantially to the overall gait trajectory computation. (3) The motor control scheme used in the local reflexes of our robot is more straightforward and has more biological plausibility than that of other robots, because the outputs of the motor neurons in our reflexive controller are directly driving the motors of the joints rather than working as references for position or velocity control. As a consequence, the neural controller and the robot mechanics are closely coupled as a neuromechanical system, and this study emphasizes that dynamically stable biped walking gaits emerge from the coupling between neural computation and physical computation. This is demonstrated by different walking experiments using a real robot as well as by a Poincare map analysis applied on a model of the robot in order to assess its stability.

[1]  F. Delcomyn.  Neural basis of rhythmic behavior in animals. , 1980, Science.

[2]  R. Full,et al.  Mechanics of six-legged runners. , 1990, The Journal of experimental biology.

[3]  Patrik Larsson,et al.  A Distributed Neural Network Architecture for Hexapod Robot Locomotion , 1992, Neural Computation.

[4]  Randall D. Beer,et al.  Biological Neural Networks in Invertebrate Neuroethology and Robotics. Editors: Randall D. Beer, Roy E. Ritzmann, Thomas McKenna (Academic Press, Inc., Harcourt Brace Jovanovich, 1993) , 1996, SGAR.

[5]  Yildirim Hurmuzlu,et al.  Dynamics of Bipedal Gait: Part II—Stability Analysis of a Planar Five-Link Biped , 1993 .

[6]  Cynthia Ferrell,et al.  A comparison of three insect-inspired locomotion controllers , 1995, Robotics Auton. Syst..

[7]  Randall D. Beer,et al.  Application of evolved locomotion controllers to a hexapod robot , 1996, Robotics Auton. Syst..

[8]  Cruse,et al.  Curve walking in crayfish , 1996, The Journal of experimental biology.

[9]  Randall D. Beer,et al.  The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment , 1997, Trends in Neurosciences.

[10]  Randall D. Beer,et al.  Biologically inspired approaches to robotics: what can we learn from insects? , 1997, CACM.

[11]  Jessica K. Hodgins,et al.  Slipping and Tripping Reflexes for Bipedal Robots , 1997, Auton. Robots.

[12]  Örjan Ekeberg,et al.  A neuro-mechanical model of legged locomotion: single leg control , 1998, Biological Cybernetics.

[13]  Richard Quint van der Linde,et al.  Active Leg Compliance for Passive Walking , 1998, ICRA.

[14]  Thomas Kindermann,et al.  Walknet--a biologically inspired network to control six-legged walking , 1998, Neural Networks.

[15]  J. F. Yang,et al.  Infant stepping: a method to study the sensory control of human walking , 1998, The Journal of physiology.

[16]  Jerry E. Pratt,et al.  Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots , 2000 .

[17]  J. G. Cham,et al.  Robust Dynamic Locomotion Through Feedforward-Preflex Interaction , 2000, Dynamic Systems and Control: Volume 2.

[18]  Daniel E. Koditschek,et al.  The Role of Reflexes Versus Central Pattern Generators in Dynamical Legged Locomotion , 2000 .

[19]  Hiroaki Funabashi,et al.  Disturbance Compensating Control of a Biped Walking Machine Based on Reflex Motions , 2001 .

[20]  M. Anthony Lewis,et al.  Certain Principles of Biomorphic Robots , 2001, Auton. Robots.

[21]  Joel L. Davis,et al.  Neurotechnology for Biomimetic Robots , 2002 .

[22]  Ralph Etienne-Cummings,et al.  An in silico central pattern generator: silicon oscillator, coupling, entrainment, and physical computation , 2003, Biological Cybernetics.

[23]  Yasuhiro Fukuoka,et al.  Adaptive Dynamic Walking of a Quadruped Robot on Irregular Terrain Based on Biological Concepts , 2003, Int. J. Robotics Res..

[24]  Florentin Wörgötter,et al.  Isotropic Sequence Order Learning , 2003, Neural Computation.

[25]  Fumiya Iida,et al.  Self-Stabilization and Behavioral Diversity of Embodied Adaptive Locomotion , 2004, Embodied Artificial Intelligence.

[26]  H. Cruse,et al.  Coordination of the legs of a slow-walking cat , 2004, Experimental Brain Research.

[27]  Gentaro Taga,et al.  A model of the neuro-musculo-skeletal system for human locomotion , 1995, Biological Cybernetics.

[28]  Florentin Wörgötter,et al.  Inside embodiment – what means embodiment to radical constructivists? , 2005 .

[29]  Fumiya Iida,et al.  Sensing through body dynamics , 2006, Robotics Auton. Syst..

[30]  Yasuhiro Fukuoka,et al.  Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based on Biological Concepts , 2007, Int. J. Robotics Res..