Learning to Perceive and Act by Trial and Error
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine 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
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Reinforcement Learning
Neuro-Dynamic Programming
Variable Resolution Discretization in Optimal Control
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)
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Dynamic Programming
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Visual feature learning
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
Decision tree methods for finding reusable MDP homomorphisms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Closed-loop learning of visual control policies
Journal of Artificial Intelligence Research
Learning visual representations for perception-action systems
International Journal of Robotics Research
Approximate policy iteration for closed-loop learning of visual tasks
ECML'06 Proceedings of the 17th European conference on Machine Learning
Task-Driven discretization of the joint space of visual percepts and continuous actions
ECML'06 Proceedings of the 17th European conference on Machine Learning
Reinforcement learning with raw image pixels as input state
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
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
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We introduce flexible algorithms that can automatically learn mappings from images to actions by interacting with their environment. They work by introducing an image classifier in front of a Reinforcement Learning algorithm. The classifier partitions the visual space according to the presence or absence of highly informative local descriptors. The image classifier is incrementally refined by selecting new local descriptors when perceptual aliasing is detected. Thus, we reduce the visual input domain down to a size manageable by Reinforcement Learning, permitting us to learn direct percept-to-action mappings. Experimental results on a continuous visual navigation task illustrate the applicability of the framework.