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Document type:
Zeitschriftenaufsatz 
Author(s):
Bing, Zhenshan; Lemke, Christian; Cheng, Long; Huang, Kai; Knoll, Alois 
Title:
Energy-efficient and damage-recovery slithering gait design for a snake-like robot based on reinforcement learning and inverse reinforcement learning 
Abstract:
Similar to real snakes in nature, the flexible trunks of snake-like robots enhance their movement capabilities and adaptabilities in diverse environments. However, this flexibility corresponds to a complex control task involving highly redundant degrees of freedom, where traditional model-based methods usually fail to propel the robots energy-efficiently and adaptively to unforeseeable joint damage. In this work, we present an approach for designing an energy-efficient and damage-recovery slithe...    »
 
Keywords:
, Reinforcement learning, Inverse reinforcement learning, Motion planning, Damage recovery 
Journal title:
Neural Networks 
Year:
2020 
Print-ISSN:
0893-6080