Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/51621
Título: | Biped locomotion - improvement and adaptation |
Autor(es): | Teixeira, Carlos José Fortunas Costa, Lino Santos, Cristina |
Palavras-chave: | Reinforcement Learning Policy Improvement Biped Locomotion Dynamic Movement Primitives Biped Locomotion and Dynamic Movement Primitives |
Data: | 2014 |
Editora: | IEEE |
Revista: | IEEE International Conference on Autonomous Robot Systems and Competitions |
Resumo(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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/51621 |
ISBN: | 978-1-4799-4253-4 |
DOI: | 10.1109/ICARSC.2014.6849771 |
ISSN: | 2573-9360 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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d41.pdf Acesso restrito! | 321,09 kB | Adobe PDF | Ver/Abrir |