SILVA, I. J.PERICO, D. H.HOMEM, T. P. D.VILAO, C. O.Reinaldo BianchiFlavio Tonidandel2022-01-122022-01-122016-01-05SILVA, 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, 2016.1865-0929https://repositorio.fei.edu.br/handle/FEI/3928© 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.Acesso RestritoArtigo de evento10.1007/978-3-319-47247-8_14Gait pattern stabilizationHumanoid robotsReinforcement learning