Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance

• URL: http://arxiv.org/abs/2410.08212v1
• Date: Wed, 25 Sep 2024 07:02:04 GMT
• Title: Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance
• Authors: Marwan Hamze, Mitsuharu Morisawa, Eiichi Yoshida,
• Abstract summary: Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. In this paper, we aim to achieve bipedal locomotion in an environment where obstacles are present using a policy-based reinforcement learning.
• Score: 0.3481985817302898
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning has seen successful implementations on humanoid robots to achieve dynamic walking. However, these implementations have been so far successful in simple environments void of obstacles. In this paper, we aim to achieve bipedal locomotion in an environment where obstacles are present using a policy-based reinforcement learning. By adding simple distance reward terms to a state of art reward function that can achieve basic bipedal locomotion, the trained policy succeeds in navigating the robot towards the desired destination without colliding with the obstacles along the way.

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