Learning Humanoid Robot Motions Through Deep Neural Networks

RoboCup Soccer 3D Simulation League (Soccer 3D) is a particularly interesting challenge concerning humanoid robot soccer. It consists of a simulation environment of a soccer match with two teams, each one composed by up to 11 simulated NAO robots [1], the official robot used for RoboCup Standard Platform League since 2008. Soccer 3D is interesting for robotics research since it involves high level multi-agent cooperative decision making while providing a physically realistic environment which requires control and signal processing techniques for robust low level skills. In the current level of evolution of Soccer 3D, motion control is a key factor in team's performance. Indeed, controlling a high degrees of freedom humanoid robot is acknowledged as one of the hardest problems in Robotics. Much effort has been devised to humanoid robot walking, where researchers have been very successful in designing control algorithms which reason about reduced order mathematical models based on the Zero Moment Point (ZMP) concept, such as the linear inverted pendulum model [2]. Nevertheless, these techniques restrict the robot to operate under a small region of its dynamics, where the assumptions of the simplified models are still valid [3, 4]. Therefore, model-based techniques are hard to use for designing highly dynamic movements, such as a long distance kick and a goalkeeper's dive to defend the goal from a fast moving ball.