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» Using Stochastic Grammars to Learn Robotic Tasks
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ICML
2009
IEEE
14 years 9 months ago
Robot trajectory optimization using approximate inference
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
Marc Toussaint
AMS
2005
Springer
112views Robotics» more  AMS 2005»
14 years 2 months ago
Combining Learning and Programming for High-Performance Robot Controllers
Abstract. The implementation of high-performance robot controllers for complex control tasks such as playing autonomous robot soccer is tedious, errorprone, and a never ending prog...
Alexandra Kirsch, Michael Beetz
ICRA
2009
IEEE
170views Robotics» more  ICRA 2009»
14 years 3 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...
ICRA
2006
IEEE
149views Robotics» more  ICRA 2006»
14 years 2 months ago
On Learning the Statistical Representation of a Task and Generalizing it to Various Contexts
— This paper presents an architecture for solving generically the problem of extracting the constraints of a given task in a programming by demonstration framework and the problem...
Sylvain Calinon, Florent Guenter, Aude Billard
HCW
1999
IEEE
14 years 27 days ago
Multiple Cost Optimization for Task Assignment in Heterogeneous Computing Systems Using Learning Automata
A framework for task assignment in heterogeneous computing systems is presented in this work. The framework is based on a learning automata model. The proposed model can be used f...
Raju D. Venkataramana, N. Ranganathan