Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning

Adaptive Quadruped Balance Control for Dynamic Environments Using Maximum-Entropy Reinforcement Learning

External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for quadrupedal self-balancing, which is adaptable to multiple unpredictable scenes of external continuous disturbance. Different from conventional method...

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Bibliographic Details
Main Authors: Haoran Sun, Tingting Fu, Yuanhuai Ling, Chaoming He
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Sensors
Subjects:
quadruped robot
multi-contact balance control
reinforcement learning (RL)
artificial neural networks (ANN)
soft actor-critic (SAC)
Online Access:https://www.mdpi.com/1424-8220/21/17/5907

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