Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Learning to Perceive and Act by Trial and Error
Machine Learning
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
C4.5: programs for machine learning
C4.5: programs for machine learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Graphical Templates for Model Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closed-Loop Object Recognition Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
ACM Computing Surveys (CSUR)
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Finite State Markovian Decision Processes
Finite State Markovian Decision Processes
Variable Resolution Discretization in Optimal Control
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Object-based queries using color points of interest
CBAIVL '01 Proceedings of the IEEE Workshop on Content-based Access of Image and Video Libraries (CBAIVL'01)
Recognition of Planar Object Classes
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Dynamic Programming
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Visual feature learning
Least-squares policy iteration
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
An effective decision procedure for linear arithmetic over the integers and reals
ACM Transactions on Computational Logic (TOCL)
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Task-Driven Learning of Spatial Combinations of Visual Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Interactive learning of mappings from visual percepts to actions
ICML '05 Proceedings of the 22nd international conference on Machine learning
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Q-learning of sequential attention for visual object recognition from informative local descriptors
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised Learning of Dense Hierarchical Appearance Represe
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Learning sequential visual attention control through dynamic state space discretization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Online learning of task-driven object-based visual attention control
Image and Vision Computing
Learning visual representations for perception-action systems
International Journal of Robotics Research
Sub-sampling: Real-time vision for micro air vehicles
Robotics and Autonomous Systems
Learning motion controllers with adaptive depth perception
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Learning motion controllers with adaptive depth perception
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Construction of approximation spaces for reinforcement learning
The Journal of Machine Learning Research
| Hi-index | 0.00 |
In this paper we present a general, flexible framework for learning mappings from images to actions by interacting with the environment. The basic idea is to introduce a feature-based image classifier in front of a reinforcement learning algorithm. The classifier partitions the visual space according to the presence or absence of few highly informative local descriptors that are incrementally selected in a sequence of attempts to remove perceptual aliasing. We also address the problem of fighting overfitting in such a greedy algorithm. Finally, we show how high-level visual features can be generated when the power of local descriptors is insufficient for completely disambiguating the aliased states. This is done by building a hierarchy of composite features that consist of recursive spatial combinations of visual features. We demonstrate the efficacy of our algorithms by solving three visual navigation tasks and a visual version of the classical "Car on the Hill" control problem.