论文标题
CTRNN和BPTT算法的实时实施以学习在线双皮机器人平衡：站立姿势的实验
Real time implementation of CTRNN and BPTT algorithm to learn on-line biped robot balance: Experiments on the standing posture
论文作者
论文摘要
本文介绍了有关连续时间复发性神经网络（CTRNN）的实时实现以及通过时间（BPTT）算法的动态反向传播的实验结果。进行实验，以控制其站立姿势中双头机器人原型的平衡。通过控制躯干的关节运动，对神经控制器进行了训练以补偿外部扰动。算法嵌入机器人的实时电子单元中。在线学习实施详细介绍。学习行为和控制绩效的结果证明了拟议方法的强度和效率。
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.
