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» Using Stochastic Grammars to Learn Robotic Tasks
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ICANN
2007
Springer
14 years 1 months ago
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
Daan Wierstra, Alexander Förster, Jan Peters,...
HIS
2007
13 years 9 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
ICRA
2010
IEEE
130views Robotics» more  ICRA 2010»
13 years 6 months ago
Predictive State Representations for grounding human-robot communication
— Allowing robots to communicate naturally with humans is a major goal for social robotics. Most approaches have focused on building high-level probabilistic cognitive models. Ho...
Eric Meisner, Sanmay Das, Volkan Isler, Jeff Trink...
ICRA
2009
IEEE
167views Robotics» more  ICRA 2009»
14 years 2 months ago
Articulated object tracking by rendering consistent appearance parts
— We describe a general methodology for tracking 3-dimensional objects in monocular and stereo video that makes use of GPU-accelerated filtering and rendering in combination wit...
Zachary A. Pezzementi, Sandrine Voros, Gregory D. ...
ICRA
2010
IEEE
158views Robotics» more  ICRA 2010»
13 years 6 months ago
Movement templates for learning of hitting and batting
Abstract— Hitting and batting tasks, such as tennis forehands, ping-pong strokes, or baseball batting, depend on predictions where the ball can be intercepted and how it can prop...
Jens Kober, Katharina Mülling, Oliver Kroemer...