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IJRR
2011
218views more  IJRR 2011»
14 years 4 months ago
Motion planning under uncertainty for robotic tasks with long time horizons
Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
ICRA
2010
IEEE
134views Robotics» more  ICRA 2010»
14 years 8 months ago
Understanding and executing instructions for everyday manipulation tasks from the World Wide Web
Service robots will have to accomplish more and more complex, open-ended tasks and regularly acquire new skills. In this work, we propose a new approach to generating plans for su...
Moritz Tenorth, Daniel Nyga, Michael Beetz
ICRA
2002
IEEE
132views Robotics» more  ICRA 2002»
15 years 2 months ago
Visually Built Task Models for Robot Teams in Unstructured Environments
In field environments it is not usually possible to provide robotic systems with valid geometric models of the task and environment. The robot or robot teams will need to create t...
Vivek A. Sujan, Steven Dubowsky
RSS
2007
176views Robotics» more  RSS 2007»
14 years 11 months ago
Active Policy Learning for Robot Planning and Exploration under Uncertainty
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
ROBOCUP
2007
Springer
134views Robotics» more  ROBOCUP 2007»
15 years 3 months ago
A Scalable Hybrid Multi-robot SLAM Method for Highly Detailed Maps
Abstract. Recent successful SLAM methods employ hybrid map representations combining the strengths of topological maps and occupancy grids. Such representations often facilitate mu...
Max Pfingsthorn, Bayu Slamet, Arnoud Visser