TY - GEN
T1 - A study of adaptive locomotive behaviors of a biped robot
T2 - 11th International Conference on the Simulation of Adaptive Behavior, SAB 2010
AU - Nassour, John
AU - Hénaff, Patrick
AU - Ben Ouezdou, Fathi
AU - Cheng, Gordon
PY - 2010
Y1 - 2010
N2 - Neurobiological studies showed the important role of Centeral Pattern Generators for spinal cord in the control and sensory feedback of animals' locomotion. In this paper, this role is taken into account in modeling bipedal locomotion of a robot. Indeed, as a rhythm generator, a non-classical model of a neuron that can generate oscillatory as well as diverse motor patterns is presented. This allows different motion patterns on the joints to be generated easily. Complex tasks, like walking, running, and obstacle avoidance require more than just oscillatory movements. Our model provides the ability to switch between intrinsic behaviors, to enable the robot to react against environmental changes quickly. To achieve complex tasks while handling external perturbations, a new space for joints' patterns is introduced. Patterns are generated by our learning mechanism based on success and failure with the concept of vigilance. This allows the robot to be prudent at the beginning and adventurous at the end of the learning process, inducing a more efficient exploration for new patterns. Motion patterns of the joint are classified into classes according to a metric, which reflects the kinetic energy of the limb. Due to the classification metric, high-level control for action learning is introduced. For instance, an adaptive behavior of the rhythm generator neurons in the hip and the knee joints against external perturbation are shown to demonstrate the effectiveness of the proposed learning approach.
AB - Neurobiological studies showed the important role of Centeral Pattern Generators for spinal cord in the control and sensory feedback of animals' locomotion. In this paper, this role is taken into account in modeling bipedal locomotion of a robot. Indeed, as a rhythm generator, a non-classical model of a neuron that can generate oscillatory as well as diverse motor patterns is presented. This allows different motion patterns on the joints to be generated easily. Complex tasks, like walking, running, and obstacle avoidance require more than just oscillatory movements. Our model provides the ability to switch between intrinsic behaviors, to enable the robot to react against environmental changes quickly. To achieve complex tasks while handling external perturbations, a new space for joints' patterns is introduced. Patterns are generated by our learning mechanism based on success and failure with the concept of vigilance. This allows the robot to be prudent at the beginning and adventurous at the end of the learning process, inducing a more efficient exploration for new patterns. Motion patterns of the joint are classified into classes according to a metric, which reflects the kinetic energy of the limb. Due to the classification metric, high-level control for action learning is introduced. For instance, an adaptive behavior of the rhythm generator neurons in the hip and the knee joints against external perturbation are shown to demonstrate the effectiveness of the proposed learning approach.
UR - http://www.scopus.com/inward/record.url?scp=78249233279&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15193-4_30
DO - 10.1007/978-3-642-15193-4_30
M3 - Conference contribution
AN - SCOPUS:78249233279
SN - 3642151922
SN - 9783642151927
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 313
EP - 324
BT - From Animals to Animats 11 - 11th International Conference on Simulation of Adaptive Behavior, SAB 2010, Proceedings
Y2 - 25 August 2010 through 28 August 2010
ER -