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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 9 of 9  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
NLP 2013-12-07
09:00
Overseas City University of Hong Kong Standing-up and Balancing Behaviors of Android Phone Robot -- Control of Spring-attached Wheeled Inverted Pendulum --
Jiexin Wang (Kyoto Univ), Eiji Uchibe, Kenji Doya (OIST)
In order to construct small-sized robots, it is promising to use Android phone as a brain with sensors. However, it is d... [more] NLP2013-122
pp.49-54
CNR 2013-10-17
10:30
Osaka Grand Front Osaka Implementation of reinforcement learning using open source software
Eiji Uchibe, Kenji Doya (OIST)
Reinforcement learning has been studied as an autonomous learning framework for robots. Several learning algorithms have... [more] CNR2013-9
pp.1-6
NC, IPSJ-BIO 2012-06-28
10:45
Okinawa OIST, Okinawa Learning Motor-visual Dynamics and Solving Linearlized Bellman Equation for Robot Control
Ken Kinjo (NAIST/OIST), Eiji Uchibe (OIST), Junichiro Yoshimoto, Kenji Doya (OIST/NAIST)
 [more] NC2012-4
pp.19-24
NC, MBE
(Joint)
2011-03-07
15:00
Tokyo Tamagawa University Robot Control based on linearized Bellman equation -- System identification and approximation of desirability function --
Ken Kinjo (NAIST/OIST), Eiji Uchibe (OIST), Junichiro Yoshimoto, Kenji Doya (OIST/NAIST)
 [more] NC2010-145
pp.107-112
NC, MBE
(Joint)
2010-03-10
14:35
Tokyo Tamagawa University Effects of environmental dynamics and observational uncertainty on CPG-based sensory feedback control.
Shinji Kimura, Mayumi Haga (NAIST/OIST), Eiji Uchibe (OIST), Junichiro Yoshimoto, Kenji Doya (NAIST/OIST)
Central pattern generators (CPG) are a popular method for the control of rhythmic locomotion in robots. The advantage is... [more] NC2009-125
pp.219-224
NC 2009-05-25
12:00
Okinawa OIST, Okinawa Reinforcement learning through the active low-dimensional sensory information
Mikihiro Kobayashi (Nara Inst. of Sci and Tech.), Eiji Uchibe, Kenji Doya (OIST)
The goal of this research is to reduce the dimension of the state spaces in the framework of
reinforcement learning usi... [more]
NC2009-4
pp.19-24
NC 2007-03-14
16:00
Tokyo Tamagawa University Action-Oriented State Coding by Neighbourhood Component Analysis
Makoto Otsuka (OIST/NAIST), Eiji Uchibe (OIST), Kenji Doya (OIST/NAIST)
 [more] NC2006-149
pp.185-190
NC 2006-06-16
09:00
Okinawa OIST Reinforcement learning under constraints generated by multiple reward functions
Eiji Uchibe, Kenji Doya (OIST)
 [more] NC2006-22
pp.1-6
NC 2006-03-16
14:55
Tokyo Tamagawa University Multiobjective Reinforcement Learning based on Multiple Value Function
Takumi Kamioka (OIST/NAIST), Eiji Uchibe (OIST), Kenji Doya (OIST/ATR)
Standard Reinforcement Learning(RL) is formulated
for optimization of a single objective function.
However in most re... [more]
NC2005-146
pp.127-132
 Results 1 - 9 of 9  /   
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