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» Using Stochastic Grammars to Learn Robotic Tasks
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ICRA
2009
IEEE
111views Robotics» more  ICRA 2009»
15 years 8 months ago
Model-based and model-free reinforcement learning for visual servoing
— To address the difficulty of designing a controller for complex visual-servoing tasks, two learning-based uncalibrated approaches are introduced. The first method starts by b...
Amir Massoud Farahmand, Azad Shademan, Martin J&au...
CVPR
2010
IEEE
15 years 9 months ago
Detecting and Parsing Architecture at City Scale from Range Data
We present a method for detecting and parsing buildings from unorganized 3D point clouds into a compact, hierarchical representation that is useful for high-level tasks. The input...
Alexander Toshev, Philippos Mordohai, Ben Taskar
AAAI
2012
13 years 3 months ago
Model Learning and Real-Time Tracking Using Multi-Resolution Surfel Maps
For interaction with its environment, a robot is required to learn models of objects and to perceive these models in the livestreams from its sensors. In this paper, we propose a ...
Jörg Stückler, Sven Behnke
RAS
2010
109views more  RAS 2010»
14 years 11 months ago
Combining active learning and reactive control for robot grasping
Grasping an object is a task that inherently needs to be treated in a hybrid fashion. The system must decide both where and how to grasp the object. While selecting where to grasp...
Oliver Krömer, Renaud Detry, Justus H. Piater...
IJCNN
2008
IEEE
15 years 7 months ago
A neural wake-sleep learning architecture for associating robotic facial emotions
—A novel wake-sleep learning architecture for processing a robot’s facial expressions is introduced. According to neuroscience evidence, associative learning of emotional respo...
Chi-Yung Yau, Kevin Burn, Stefan Wermter