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» Metacognitive Control and Optimal Learning
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ESSMAC
2003
Springer
15 years 2 months ago
Nonlinear Predictive Control with a Gaussian Process Model
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
Jus Kocijan, Roderick Murray-Smith
ECAI
2004
Springer
15 years 3 months ago
Avatars That Learn How to Behave
It is possible to model avatars that learn to simulate object manipulations and other complex actions. A number of applications may benefit from this technique including safety, e...
Adam Szarowicz, Paolo Remagnino
FLAIRS
2004
14 years 11 months ago
Developing Task Specific Sensing Strategies Using Reinforcement Learning
Robots that can adapt and perform multiple tasks promise to be a powerful tool with many applications. In order to achieve such robots, control systems have to be constructed that...
Srividhya Rajendran, Manfred Huber
ECAL
2005
Springer
15 years 3 months ago
A Dynamical Systems Approach to Learning: A Frequency-Adaptive Hopper Robot
We present an example of the dynamical systems approach to learning and adaptation. Our goal is to explore how both control and learning can be embedded into a single dynamical sys...
Jonas Buchli, Ludovic Righetti, Auke Jan Ijspeert
102
Voted
SAB
2010
Springer
147views Optimization» more  SAB 2010»
14 years 8 months ago
Fractal Gene Regulatory Networks for Robust Locomotion Control of Modular Robots
Designing controllers for modular robots is difficult due to the distributed and dynamic nature of the robots. In this paper fractal gene regulatory networks are evolved to control...
Payam Zahadat, David Johan Christensen, Ulrik Pagh...