DeepGait: Planning and Control of Quadrupedal Gaits Using Deep Reinforcement Learning

This letter addresses the problem of legged locomotion in non-flat terrain. As legged robots such as quadrupeds are to be deployed in terrains with geometries which are difficult to model and predict, the need arises to equip them with the capability to generalize well to unforeseen situations. In t...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 5; H. 2; S. 3699 - 3706
Hauptverfasser:	Tsounis, Vassilios, Alge, Mitja, Lee, Joonho, Farshidian, Farbod, Hutter, Marco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Computer simulation Deep learning deep learning in robotics and automation Legged locomotion Legged robots Locomotion Machine learning Markov processes motion and path planning Motion planning Optimization Path planning Physical simulation Policies Robot dynamics Terrain
ISSN:	2377-3766, 2377-3766
Online-Zugang:	Volltext
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