Learning CPG-based Biped Locomotion with a Policy Gradient Method: Application to a Humanoid Robot
International Journal of Robotics Research
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Much of the literature shows that Central Pattern Generators (CPGs) are a good approach for generating periodic motions for legged robots. In most of the presented works the numerous CPG parameters are set by automatic techniques like genetic algorithms. This gives the user little control over the resulting motions, since all of the desired features of the motion must be encoded by a fitness/score function. In this paper we present the idea of setting the CPG parameters by interaction with the user, in particular by using the tactile interaction. Key elements of the system are a CPG network, a touch protocol and a self-collision prevention system. In this paper we present a practical implementation of each element that confirms the feasibility of the method.