![]() utilised an on-robot DRL method with minimal human interference by constructing a physical reset mechanism quite similar to that of a computer simulation and achieved trotting and walking on unstructured terrain. ![]() first implemented DRL-trained walking, trotting, and galloping on a real Minitaur robot and verified the feasibility of the end-to-end route. achieved multiple gaits, including running and bounding, with the help of a predefined trajectory generator. In search of higher generalising performance and agility, learning-based approaches, such as the deep reinforcement learning (DRL), have gained new trends in legged locomotion control to solve these problems because they allow learning multiple input and multiple output feedback control policies that can run in real-time, particularly dealing with very high dimensional sensory inputs.Ĭonstrained by the capability of computing devices and legged robots, DRL has not been applied to motion control for quadruped robots until recent years. Methods based on reduce-order models are proven to be feasible for generating adaptive gaits for real robots using prior knowledge and fine-tuning. The motion planning and control of a legged robot has been well researched. Legged robots attracted more attention in recent years for their versatile motion capabilities. The authors’ approach shows efficient computing and good locomotion results by the Jueying Mini quadrupedal robot bounding over uneven terrain. A variety of environments are presented both indoors and outdoors with the authors’ approach. The NN-based feedback controller was learned in the simulation and directly deployed on the real quadruped robot Jueying Mini successfully. 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 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). The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. IET Generation, Transmission & Distributionīounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles.IET Electrical Systems in Transportation. ![]() ![]()
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