  Bounding is one of the important gaits in quadrupedal locomotion for
negotiating obstacles. The authors proposed an effective approach that can
learn robust bounding gaits more efficiently despite its large variation in
dynamic 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 considering
contact points and phases to enforce the gait symmetry and periodicity, which
improved the bounding performance. The NN based feedback controller was learned
in the simulation and directly deployed on the real quadruped robot Jueying
Mini successfully. A variety of environments are presented both indoors and
outdoors with the authors approach. The authors approach shows efficient
computing and good locomotion results by the Jueying Mini quadrupedal robot
bounding over uneven terrain.
