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» Learning to Predict Slip for Ground Robots
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ICRA
2006
IEEE
87views Robotics» more  ICRA 2006»
13 years 11 months ago
Learning to Predict Slip for Ground Robots
— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do t...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
ICRA
2010
IEEE
130views Robotics» more  ICRA 2010»
13 years 3 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...
RAS
2008
123views more  RAS 2008»
13 years 4 months ago
Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping
This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates th...
Martin Persson, Tom Duckett, Achim J. Lilienthal
ISCIS
2009
Springer
13 years 11 months ago
Predicting future object states using learned affordances
Abstract—The notion of affordances was proposed by J.J. Gibson, to refer to the action possibilities offered to the organism by its environment. In a previous formalization, affo...
Emre Ugur, Erol Sahin, Erhan Oztop
AROBOTS
2011
12 years 12 months ago
Learning GP-BayesFilters via Gaussian process latent variable models
Abstract— GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filt...
Jonathan Ko, Dieter Fox