Biologically inspired layered learning in humanoid robots

摘要

A hierarchical paradigm for bipedal walking which consists of 4 layers of learning is introduced in this paper. In the Central Pattern Generator layer some Learner-CPGs are trained which are made of coupled oscillatory neurons in order to generate basic walking trajectories. The dynamical model of each neuron in Learner-CPGs is discussed. Then we explain how we have connected these new neurons with each other and built up a new type of neural network called Learner-CPG neural networks. Training method of these neural networks is the most important contribution of this paper. The proposed two-stage learning algorithm consists of learning the basic frequency of the input trajectory to find a suitable initial point for the second stage. In the next stage a mathematical path to the best unknown parameters of the neural network is designed. Then these neural networks are trained with some basic trajectories enable them to generate new walking patterns based on a policy. A policy of walking is parameterized by some policy parameters controlling the central pattern generator variables. The policy learning can take place in a middle layer called MLR layer. High level commands are originated from a third layer called HLDU layer. In this layer the focus is on training curvilinear walking in NAO humanoid robot. This policy should optimize total payoff of a walking period which is defined as a combination of smoothness, precision and speed.