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

Patrick Henaff (1) , Vincent Scesa , Fethi Ben Ouezdou (2) , Olivier Bruneau (2)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 LISV - Laboratoire d'Ingénierie des Systèmes de Versailles

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

Mots clés: Neural control; Learning algorithms; CTRNN; Real-time systems; Robotics; Biped robot; INDEX TERMS: Neural control; Legged locomotion; robotics

Domaines: Robotique [cs.RO]

Patrick Henaff, Vincent Scesa, Fethi Ben Ouezdou, Olivier Bruneau. Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture. Control Engineering Practice, 2010, 19 (1), pp.89 - 99. ⟨10.1016/j.conengprac.2010.10.002⟩. ⟨hal-00538710⟩