Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Sugimoto, Norikazu"'
While MPC enables nonlinear feedback control by solving an optimal control problem at each timestep, the computational burden tends to be significantly large, making it difficult to optimize a policy within the control period. To address this issue,
Externí odkaz:
http://arxiv.org/abs/2410.04929
Autor:
Sugimoto, Norikazu, Tangkaratt, Voot, Wensveen, Thijs, Zhao, Tingting, Sugiyama, Masashi, Morimoto, Jun
In this study, we show that a movement policy can be improved efficiently using the previous experiences of a real robot. Reinforcement Learning (RL) is becoming a popular approach to acquire a nonlinear optimal policy through trial and error. Howeve
Externí odkaz:
http://arxiv.org/abs/1405.2406
Publikováno v:
Frontiers in Robotics & AI; 2023, p1-13, 13p
Publikováno v:
In Neural Networks May 2012 29-30:8-19
Autor:
Sugimoto, Norikazu1,2 xsugi@nict.go.jp, Haruno, Masahiko3,4,5 mharuno@nict.go.jp, Doya, Kenji6 doya@oist.jp, Kawato, Mitsuo7 kawato@atr.jp, Barto, Andrew
Publikováno v:
Neural Computation. Mar2012, Vol. 24 Issue 3, p577-606. 30p. 3 Diagrams, 7 Graphs.
Akademický článek
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Autor:
Sugimoto, Norikazu, Tangkaratt, Voot, Wensveen, Thijs, Zhao, Tingting, Sugiyama, Masashi, Morimoto, Jun
Publikováno v:
2014 IEEE-RAS International Conference on Humanoid Robots; 2014, p554-559, 6p
Autor:
Vuga, Rok, Ogrinc, Matjaz, Gams, Andrej, Petric, Tadej, Sugimoto, Norikazu, Ude, Ales, Morimoto, Jun
Publikováno v:
2013 IEEE International Conference on Robotics & Automation; 2013, p5284-5290, 7p
Autor:
Sugimoto, Norikazu, Morimoto, Jun
Publikováno v:
2013 IEEE International Conference on Robotics & Automation; 2013, p1311-1316, 6p
Autor:
Sugimoto, Norikazu, Morimoto, Jun
Publikováno v:
2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids); 2013, p429-434, 6p