Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/51613

TitleAdapting biped locomotion to sloped environments
Author(s)André, João
Teixeira, Carlos
Santos, Cristina
Costa, Lino
KeywordsReinforcement learning
Biped locomotion
Movement adaptation category (1)
Issue dateDec-2015
PublisherSpringer
JournalJournal of Intelligent & Robotic Systems
Abstract(s)In this work, reinforcement learning techniques are implemented and compared to address biped locomotion optimization. Central Pattern Generators (CPGs) and Dynamic Movement Primitives (DMPs) were combined to easily produce complex trajectories for the joints of a simulated DARwIn-OP humanoid robot. Two reinforcement learning algorithms, Policy Learning by Weighting Exploration with the Returns (PoWER) and Path Integral Policy Improvement with Covariance Matrix Adaptation (PI2-CMA) were implemented in the simulated DARwIn-OP to seek optimal DMP parameters that maximize frontal velocity when facing different situations which demand adaptation from the controller in order to successfully walk in different types of slopes. Additionally, elitism was introduced in PI2-CMA in order to improve the performance of the algorithm. Results show that these approaches enabled easy adaptation of DARwIn-OP to new situations. The results are very promising and demonstrate flexibility at generating or adapting new trajectories for locomotion.
TypeArticle
URIhttp://hdl.handle.net/1822/51613
DOI10.1007/s10846-015-0196-0
ISSN0921-0296
1573-0409
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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