Dynamic Obstacle Avoidance of Hexapod Robot Based on Incremental Reinforcement Learning
DENG Guizhou
FU Huiqiao
WANG Xinpeng
TANG Kaiqiang
LIU Canghai
Abstract：Deep reinforcement learning can enable the hexapod robot to obtain an optimal obstacle avoidance policy through training in a complex environment to complete the obstacle avoidance task.However,after the obstacles change in the environment,it often takes a lot of time to retrain the obstacle avoidance policy of the hexapod robot.Therefore,the dynamic obstacle avoidance of hexapod robot is still a challenging task.In this paper,a dynamic obstacle avoidance model of hexapod robot based on incremental reinforcement learning framework(IRL)is proposed to realize dynamic obstacle avoidance of hexapod robot.Firstly,the learned ob-stacle avoidance policy is relaxed through the policy relaxation mechanism to avoid policy falling into local optima.Secondly,the ef-fective states are given higher weights by importance weighting mechanism to update the obstacle avoidance policy to speed up the training process of the obstacle avoidance policy.The experimental results show that the proposed obstacle avoidance model of hexa-pod robot can adjust the obstacle avoidance policy of the hexapod robot more effectively than the baseline method after the obstacle changes.
Keywords：hexapod robotdynamic obstacle avoidancedeep reinforcement learningincremental reinforcement learning
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：8( 3305-3312 )