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Bounding is one of the important gaits in quadrupedal locomotion fornegotiating obstacles. The authors proposed an effective approach that canlearn robust bounding gaits more efficiently despite its large variation indynamic body movements. The authors first pretrained the neural network (NN)based on data from a robot operated by conventional model based controllers,and then further optimised the pretrained NN via deep reinforcement learning(DRL). In particular, the authors designed a reward function consideringcontact points and phases to enforce the gait symmetry and periodicity, whichimproved the bounding performance. The NN based feedback controller was learnedin the simulation and directly deployed on the real quadruped robot JueyingMini successfully. A variety of environments are presented both indoors andoutdoors with the authors approach. The authors approach shows efficientcomputing and good locomotion results by the Jueying Mini quadrupedal robotbounding over uneven terrain.

Efficient learning of robust quadruped bounding using pretrained neural networks