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.

翻译：弹跳是四足机器人跨越障碍物的重要步态之一。作者提出了一种有效方法，能够在动态身体运动存在较大变化的情况下，更高效地学习稳健的弹跳步态。该方法首先基于传统模型控制器操作机器人所产生的数据，对神经网络（NN）进行预训练；随后，通过深度强化学习（DRL）进一步优化预训练的神经网络。具体而言，作者设计了一个考虑接触点与接触相位的奖励函数，以强化步态的对称性和周期性，从而提升弹跳性能。该基于神经网络的反馈控制器在仿真环境中完成训练，并成功直接部署于真实四足机器人"绝影Mini"上。作者的方法在室内外多种环境中均进行了验证。实验结果表明，该方法计算效率高，且能使"绝影Mini"四足机器人在不平坦地形上实现良好的弹跳运动效果。