Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks

• URL: http://arxiv.org/abs/2011.00446v3
• Date: Sun, 29 Oct 2023 14:18:20 GMT
• Title: Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks
• Authors: Zhicheng Wang, Anqiao Li, Yixiao Zheng, Anhuan Xie, Zhibin Li, Jun Wu, Qiuguo Zhu
• Abstract summary: 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. The authors approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain.
• Score: 15.09037992110481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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