RLOC: Terrain-Aware Legged Locomotion Using Reinforcement Learning and Optimal Control

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a re...

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Veröffentlicht in:	IEEE transactions on robotics Jg. 38; H. 5; S. 2908 - 2927
Hauptverfasser:	Gangapurwala, Siddhant, Geisert, Mathieu, Orsolino, Romeo, Fallon, Maurice, Havoutis, Ioannis
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	AI-based methods Computational modeling deep learning in robotics and automation Learning Legged locomotion legged robots Locomotion Optimal control Perturbation Physical properties Planning Policies Quadrupedal robots Robots robust/adaptive control of robotic systems Terrain Tracking Tracking control Training
ISSN:	1552-3098, 1941-0468
Online-Zugang:	Volltext
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