Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance - Laboratoire d'Ingénierie des Systèmes de Versailles
Communication Dans Un Congrès Année : 2024

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

Résumé

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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Dates et versions

hal-04618129 , version 1 (24-09-2024)

Identifiants

  • HAL Id : hal-04618129 , version 1

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Marwan Hamze, Mitsuharu Morisawa, Eiichi Yoshida. Learning Bipedal Walking for Humanoid Robots in Challenging Environments with Obstacle Avoidance. Robomech, May 2024, Utsunomiya, Japan. ⟨hal-04618129⟩
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