Abstract
This paper is concerned with locomotion problems for the quadruped robot in unknown and unstructured terrains, utilizing the emerging deep reinforcement learning technique. The state-of-the-art deep deterministic policy gradient (DDPG) algorithm is leveraged to acquire the gait policy by learning from interactions with the environment. The framework of learning system is presented based on the actor-critic architecture, and the additional domain-specific knowledge is exploited for shaping the reward function to enhance the learning efficiency during training. Moreover, a simulation study is implemented to validate the performance of the proposed DDPG-based controller.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
