Humanoid robot gait on sloping floors using reinforcement learning
Nenhuma Miniatura disponível
Citações na Scopus
2
Tipo de produção
Artigo de evento
Data
2016-01-05
Autores
SILVA, I. J.
PERICO, D. H.
HOMEM, T. P. D.
VILAO, C. O.
Reinaldo Bianchi
Flavio Tonidandel
PERICO, D. H.
HOMEM, T. P. D.
VILAO, C. O.
Reinaldo Bianchi
Flavio Tonidandel
Orientador
Periódico
Communications in Computer and Information Science
Título da Revista
ISSN da Revista
Título de Volume
Citação
SILVA, I. J.; PERICO, D. H.; HOMEM, T. P. D.; VILAO, C. O.; BIANCHI, R.; TONIDANDEL, F. Humanoid robot gait on sloping floors using reinforcement learning. Communications in Computer and Information Science, v. 619, p. 228-246, 2016.
Texto completo (DOI)
Palavras-chave
Resumo
© Springer International Publishing AG 2016.Climbing ramps is an important ability for humanoid robots: ramps exist everywhere in the world, such as in accessibility ramps and building entrances. This works proposes the use of Reinforcement Learning to learn the action policy that will make a robot walk in an upright position, in a lightly sloped terrain. The proposed architecture of our system is a two-layer combination of the traditional gait generation control loop with a reinforcement learning component. This allows the use of an accelerometer to generate a correction for the gait, when the slope of the floor where the robot is walking changes. Experiments performed on a real robot showed that the proposed architecture is a good solution for the stability problem.