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Humanoid robot gait on sloping floors using reinforcement learning

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Tipo de produção

Artigo de evento

Data de publicação

2016-01-05

Texto completo (DOI)

Periódico

Communications in Computer and Information Science

Editor

Citações na Scopus

2

Autores

SILVA, I. J.
PERICO, D. H.
HOMEM, T. P. D.
VILAO, C. O.
Reinaldo Bianchi
Flavio Tonidandel

Orientadores

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.

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.

Palavras-chave

Keywords

Gait pattern stabilization; Humanoid robots; Reinforcement learning

Assuntos Scopus

Action policies; Gait generation; Gait pattern; Humanoid robot; Proposed architectures; Sloped terrains; Stability problem; Upright position

Coleções

Avaliação

Revisão

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