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This paper describes experimental results regarding the real timeimplementation of continuous time recurrent neural networks (CTRNN) and thedynamic back-propagation through time (BPTT) algorithm for the on-line learningcontrol laws. Experiments are carried out to control the balance of a bipedrobot prototype in its standing posture. The neural controller is trained tocompensate for external perturbations by controlling the torso's joint motions.Algorithms are embedded in the real time electronic unit of the robot. On-linelearning implementations are presented in detail. The results on learningbehavior and control performance demonstrate the strength and the efficiency ofthe proposed approach.

Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture