Stable Jumping Control Based on Deep Reinforcement Learning for a Locust-Inspired Robot.

ABSTRACT: Biologically inspired jumping robots exhibit exceptional movement capabilities and can quickly overcome obstacles. However, the stability and accuracy of jumping movements are significantly compromised by rapid changes in posture. Here, we propose a stable jumping control algorithm for a locust-inspired jumping robot based on deep reinforcement learning. The algorithm utilizes a training framework comprising two neural network modules (actor network and critic network) to enhance training performance. The framework can control jumping by directly mapping the robot's observations (robot position and velocity, obstacle position, target position, etc.) to its joint torques. The control policy increases randomness and exploration by introducing an entropy term to the policy function. Moreover,

SUBMITTER: Zhou Q 

PROVIDER: S-EPMC11430585 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature