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» Learning hierarchical task networks by observation
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TSMC
2008
162views more  TSMC 2008»
14 years 9 months ago
Codevelopmental Learning Between Human and Humanoid Robot Using a Dynamic Neural-Network Model
The paper examines characteristics of interactive learning between human tutors and a robot having a dynamic neural network model which is inspired by human parietal cortex functio...
Jun Tani, Ryunosuke Nishimoto, Jun Namikawa, Masat...
81
Voted
ICRA
2009
IEEE
170views Robotics» more  ICRA 2009»
15 years 4 months ago
Imitation learning with generalized task descriptions
— In this paper, we present an approach that allows a robot to observe, generalize, and reproduce tasks observed from multiple demonstrations. Motion capture data is recorded in ...
Clemens Eppner, Jürgen Sturm, Maren Bennewitz...
ATAL
2009
Springer
15 years 4 months ago
Adaptive learning in evolving task allocation networks
In this paper, we study multi-agent economic systems using a recent approach to economic modeling called Agent-based Computational Economics (ACE): the application of the Complex ...
Tomas Klos, Bart Nooteboom
TSMC
2008
117views more  TSMC 2008»
14 years 8 months ago
Discovery of High-Level Behavior From Observation of Human Performance in a Strategic Game
This paper explores the issues faced in creating a sys-4 tem that can learn tactical human behavior merely by observing5 a human perform the behavior in a simulation. More specific...
Brian S. Stensrud, Avelino J. Gonzalez
ICRA
2010
IEEE
128views Robotics» more  ICRA 2010»
14 years 8 months ago
A game-theoretic procedure for learning hierarchically structured strategies
— This paper addresses the problem of acquiring a hierarchically structured robotic skill in a nonstationary environment. This is achieved through a combination of learning primi...
Benjamin Rosman, Subramanian Ramamoorthy