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IROS
2006
IEEE
107views Robotics» more  IROS 2006»
15 years 3 months ago
Learning Sensory-Motor Maps for Redundant Robots
— Humanoid robots are routinely engaged in tasks requiring the coordination between multiple degrees of freedom and sensory inputs, often achieved through the use of sensorymotor...
Manuel Lopes, José Santos-Victor
76
Voted
FSR
2003
Springer
123views Robotics» more  FSR 2003»
15 years 2 months ago
Learning Predictions of the Load-Bearing Surface for Autonomous Rough-Terrain Navigation in Vegetation
Current methods for off-road navigation using vehicle and terrain models to predict future vehicle response are limited by the accuracy of the models they use and can suffer if th...
Carl Wellington, Anthony Stentz
ESANN
2008
14 years 11 months ago
Learning Inverse Dynamics: a Comparison
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexiti...
Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Ber...
96
Voted
DAGSTUHL
2003
14 years 11 months ago
Removing Some 'A' from AI: Embodied Cultured Networks
We embodied networks of cultured biological neurons in simulation and in robotics. This is a new research paradigm to study learning, memory, and information processing in real tim...
Douglas J. Bakkum, Alexander C. Shkolnik, Guy Ben-...
ICML
2007
IEEE
15 years 10 months ago
Bottom-up learning of Markov logic network structure
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
Lilyana Mihalkova, Raymond J. Mooney