论文成果

影响因子：

4.6

DOI码：

10.1109/LRA.2024.3349934

发表刊物：

IEEE ROBOTICS AND AUTOMATION LETTERS

刊物所在地：

UNITED STATES

摘要：

Domain randomization introduces perturbations in the simulation to make controllers less susceptible to the reality gap, which enables remarkable sim-to-real transfer on real quadruped robots. However, aleatoric uncertainty originating from perturbations could often lead to suboptimal controllers. In this work, we present a novel algorithm called Distributional Ensemble Actor-Critic (DEAC) that blends three ideas: distributional representation of a critic, lower bounds of the value distribution, and ensembling of multiple critics and actors. Distributional representation and ensembling provide reasonable uncertainty estimates, while lower bounds of the value distribution offer finer-grained error control. The simulation results show that the controller trained by DEAC outperforms the other baselines in the domain randomization setting. The trained controller is deployed on an A1-like robot, demonstrating high-speed running and the ability to traverse diverse terrains such as slippery plates, grassland, and wet dirt.

学科门类：

工学

卷号：

9(2)

页面范围：

1811-1818

ISSN号：

2377-3766

是否译文：

否

发表时间：

2024

发布期刊链接：

https://ieeexplore.ieee.org/document/10380686