Multimodal bipedal locomotion generation with passive dynamics via deep reinforcement learning.

ABSTRACT: Generating multimodal locomotion in underactuated bipedal robots requires control solutions that can facilitate motion patterns for drastically different dynamical modes, which is an extremely challenging problem in locomotion-learning tasks. Also, in such multimodal locomotion, utilizing body morphology is important because it leads to energy-efficient locomotion. This study provides a framework that reproduces multimodal bipedal locomotion using passive dynamics through deep reinforcement learning (DRL). An underactuated bipedal model was developed based on a passive walker, and a controller was designed using DRL. By carefully planning the weight parameter settings of the DRL reward function during the learning process based on a curriculum learning method, the bipedal model successfull

SUBMITTER: Koseki S 

PROVIDER: S-EPMC9899902 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature