Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/51621

TitleBiped locomotion - improvement and adaptation
Author(s)Teixeira, Carlos José Fortunas
Costa, Lino
Santos, Cristina
KeywordsReinforcement Learning
Policy Improvement
Biped Locomotion
Dynamic Movement Primitives
Biped Locomotion and Dynamic Movement Primitives
Issue date2014
PublisherIEEE
Abstract(s)An approach addressing biped locomotion optimization is here introduced. Concepts from Central Pattern Generators (CPGs) and Dynamic Movement Primitives (DMPs) were combined to easily produce complex trajectories for the joints of a simulated DARwIn-OP. A Reinforcement Learning Algorithm, Policy Learning by Weighting Exploration with the Returns (PoWER), was implemented to improve the robot's locomotion through exploration and evaluation of the DMPs' weights. Maximization of the DARwIn-OP's frontal velocity while performing several tasks was addressed and results show velocities up to 0.25 m / s. The Stability and Harmony metrics were included in the evaluation and both charateristics were improved by the PoWER algorithm. The results are very promising and demonstrate the approach's flexibility at generating or adapting trajectories for locomotion.
TypeConference paper
URIhttp://hdl.handle.net/1822/51621
ISBN978-1-4799-4253-4
DOI10.1109/ICARSC.2014.6849771
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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