Evolutionary Behavior Acquisition for Humanoid Robots Deniz Aydemir 1 , Hitoshi Iba 1 1 Graduate School of Frontier Sciences, Department of Frontier Informatics Tokyo University, Tokyo, Japan {deniz, iba}@iba.k.u-tokyo.ac.jp http://www.iba.k.u-tokyo.ac.jp/english/ Abstract. This paper describes and analyzes a series of experiments to develop a general evolutionary behavior acquisition technique for humanoid robots. The robot’s behavior is defined by joint controllers evolved concurrently. Each joint controller consists of a series of primitive actions defined by a chromosome. By using genetic algorithms with specifically designed genetic operators and novel representations, complex behaviors are evolved from the primitive actions defined. Representations are specifically tailored to be useful in trajectory generation for humanoid robots. The effectiveness of the method is demonstrated by two experiments: a handstand and a limbo dance behavioral tasks (leaning the body backwards so as to pass under a fixed height bar). 1 Introduction The recent remarkable progression of robotics research makes highly precise and advanced robots available today. Despite the availability of sophisticated robots, acquisition of behavioral tasks remains as a big hurdle in the field. Currently, several approaches are prominent in evolutionary behavior acquisition. [1], [2], [3] investigate appropriate neural network architectures using genetic algorithms for the adjustment of network parameters. Authors of these papers try to evolve behavioral tasks mainly based on navigation in a constructed environment for the wheeled robot Khepera. Although the results from these experiments are promising in terms of conceptual findings, there exist very few applications of the neuro-evolutionary techniques for more complex and high mobility robots such as humanoid robots. Another approach in evolutionary behavior acquisition is evolutionary gait optimization undertaken by the authors [4], [5]. These experiments involve optimization of a readily available controller for a previously specified behavior, such as quadruple walking. Main drawback here is the assumption that a hand designed controller is readily available. In this paper, we take a slightly different approach than the techniques discussed above. Rather than optimizing a hand designed controller or trying to evolve primitive behaviors conceptualized with neural networks, we consider the behavior acquisition task as a combinatorial optimization task where the task at hand is decomposable into primitive actions, and the goal is to find the