Adaptive Slope Locomotion with Deep Reinforcement Learning

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In this paper we present a model free Deep Reinforcement Learning based approach to the motion planning problem of a quadruped moving from a flat to an inclined plane. In our implementation, we do not provide any prior information of the location of the inclined plane, nor pass any vision data during the training process. With this approach, we train a 12 degree of freedom quadruped robot to traverse up and down a variety of simulated sloped environments, in the process demonstrating that deep reinforcement learning is able to generate highly dynamic and adaptable solutions.

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