Policy gradient learning for a humanoid soccer robot
Robotics and Autonomous Systems
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This paper presents a solution to the biped locomotion problem. The robot used for the experiments is robot Nao by Aldebaran Robotics and it is simulated in Webots mobile robot simulator. Our method of solution does not requires the dynamic model of the robot, thus making this approach usable to other biped robots. For faster results the number of degrees of freedom is kept low, only six are used. The walking gait is generated using Central Pattern Generators with limit-cycle oscillators. For the oscillator connection weights required for synchronization, a genetic algorithm is implemented. Our solution is generated automatically and the best results allow the robot to walk twice as fast as the Aldebaran's webots walk and four times faster than the default walk in Robotstadium.