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Learning to run naturally: guiding policies with the Spring-Loaded Inverted Pendulum

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hdl:2117/348196

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Ordoñez Apraez, Daniel Felipe
Tutor / directorMartín Muñoz, MarioMés informacióMés informacióMés informació; Moreno-Noguer, FrancescMés informació
CovenanteeUniversitat de Barcelona; Universitat Rovira i Virgili
Document typeMaster thesis
Date2021-04-28
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
In this work, we proposed a new approach for learning legged locomotion for any legged robot, in the sagittal plane, by using a combination of classical control techniques and reinforcement learning. Specifically, we use optimal control of the low-order model Spring-Loaded Inverted Pendulum (SLIP), for the planning and generation of expert reference trajectories that resemble the ideal dynamics of animals in nature, and a control policy that learns to imitate these ideal dynamics. The objective of this approach is to provide a generic methodology for learning legged locomotion, that is flexible enough to be applied to robots with different morphological properties, and reduces the impact of simulation inaccuracies in the emergence of unnatural controlled gaits.
SubjectsReinforcement learning, Aprenentatge per reforç, Robots -- Locomoció
DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)
URIhttp://hdl.handle.net/2117/348196
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  • Màsters oficials - Master in Artificial Intelligence - MAI [335]
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