Perception-Driven Learning of High-Dynamic Jumping Motions for Single-Legged Robots  

作　　者：Nengxiang Sun Fei Meng Sai Gu Botao Liu Xuechao Chen Zhangguo Yu Qiang Huang 

机构地区：[1]School of Mechatronical Engineering,Beijing Institute of Technology,Beijing,100081,China [2]Key Laboratory of Biomimetic Robots and Systems,Ministry of Education,Beijing,100081,China

出　　处：《Journal of Bionic Engineering》2024年第4期1733-1746,共14页仿生工程学报（英文版）

基　　金：supported by the National Key Research Program of China 2018AAA0100103.

摘　　要：Legged robots show great potential for high-dynamic motions in continuous interaction with the physical environment,yet achieving animal-like agility remains significant challenges.Legged animals usually predict and plan their next locomotion by combining high-dimensional information from proprioception and exteroception,and adjust the stiffness of the body’s skeletal muscle system to adapt to the current environment.Traditional control methods have limitations in handling high-dimensional state information or complex robot motion that are difficult to plan manually,and Deep Reinforcement Learning(DRL)algorithms provide new solutions to robot motioncontrol problems.Inspired by biomimetics theory,we propose a perception-driven high-dynamic jump adaptive learning algorithm by combining DRL algorithms with Virtual Model Control(VMC)method.The robot will be fully trained in simulation to explore its motion potential by learning the factors related to continuous jumping while knowing its real-time jumping height.The policy trained in simulation is successfully deployed on the bio-inspired single-legged robot testing platform without further adjustments.Experimental results show that the robot can achieve continuous and ideal vertical jumping motion through simple training.

关 键 词：Deep reinforcement learning High-dynamic jump Perception driven Single-legged robot 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] Q811[自动化与计算机技术—控制科学与工程]