ALVINN: an autonomous land vehicle in a neural network
Advances in neural information processing systems 1
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Appearance-Based Obstacle Detection with Monocular Color Vision
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Vision-based neural network road and intersection detection and traversal
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
High speed obstacle avoidance using monocular vision and reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Interactive robot task training through dialog and demonstration
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Gibsonian Affordances for Roboticists
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Teachable robots: Understanding human teaching behavior to build more effective robot learners
Artificial Intelligence
Hybrid Generative-Discriminative Visual Categorization
International Journal of Computer Vision
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
Efficient training of artificial neural networks for autonomous navigation
Neural Computation
Boosted Bayesian network classifiers
Machine Learning
Functional object class detection based on learned affordance cues
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
IEEE Transactions on Robotics
Learning Object Affordances: From Sensory--Motor Coordination to Imitation
IEEE Transactions on Robotics
The learning of adjectives and nouns from affordance and appearance features
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Robotics and Autonomous Systems
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A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment. The set of affordances define the actions that are available to the agent given the robotâ聙聶s context. A standard approach to affordance learning is direct perception, which learns direct mappings from sensor measurements to affordance labels. For example, a robot designed for cross-country navigation could map stereo depth information and image features directly into predictions about the traversability of terrain regions. While this approach can succeed for a small number of affordances, it does not scale well as the number of affordances increases. In this paper, we show that visual object categories can be used as an intermediate representation that makes the affordance learning problem scalable. We develop a probabilistic graphical model which we call the Categoryâ聙聰Affordance (CA) model, which describes the relationships between object categories, affordances, and appearance. This model casts visual object categorization as an intermediate inference step in affordance prediction. We describe several novel affordance learning and training strategies that are supported by our new model. Experimental results with indoor mobile robots evaluate these different strategies and demonstrate the advantages of the CA model in affordance learning, especially when learning from limited size data sets.