Learning Robust and Agile Legged Locomotion Using Adversarial Motion Priors

Developing both robust and agile locomotion skills for legged robots is non-trivial. In this work, we present the first blind locomotion system capable of traversing challenging terrains robustly while moving rapidly over natural terrains. Our approach incorporates the Adversarial Motion Priors (AMP...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE robotics and automation letters Ročník 8; číslo 8; s. 1 - 8
Hlavní autoři: Wu, Jinze, Xin, Guiyang, Qi, Chenkun, Xue, Yufei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Behavioral sciences
Coders
Legged locomotion
Legged Robots
Locomotion
Machine Learning for Robot Control
Reinforcement Learning
Robot dynamics
Robot sensing systems
Robots
Robustness
Sensors
Task analysis
Training
ISSN:2377-3766, 2377-3766
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Developing both robust and agile locomotion skills for legged robots is non-trivial. In this work, we present the first blind locomotion system capable of traversing challenging terrains robustly while moving rapidly over natural terrains. Our approach incorporates the Adversarial Motion Priors (AMP) in locomotion policy training and demonstrates zero-shot generalization from the motion dataset on flat terrains to challenging terrains in the real world. We show this result on a quadruped robot Go1 using only proprioceptive sensors consisting of the IMU and joint encoders. Experiments on the Go1 demonstrate the robust and natural motion generated by the proposed method for traversing challenging terrains while moving rapidly over natural terrains.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3290509