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AAAI
2008
13 years 7 months ago
An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure
When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster b...
Masahiro Ono, Brian C. Williams
ICML
2001
IEEE
14 years 6 months ago
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a...
Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, ...
ATAL
2010
Springer
13 years 6 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
CVPR
2004
IEEE
14 years 7 months ago
Atlanta World: An Expectation Maximization Framework for Simultaneous Low-Level Edge Grouping and Camera Calibration in Complex
Edges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene under...
Grant Schindler, Frank Dellaert
IROS
2007
IEEE
144views Robotics» more  IROS 2007»
13 years 11 months ago
Global action selection for illumination invariant color modeling
— A major challenge in the path of widespread use of mobile robots is the ability to function autonomously, learning useful features from the environment and using them to adapt ...
Mohan Sridharan, Peter Stone