Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
A maximum entropy approach to natural language processing
Computational Linguistics
Active learning for vision-based robot grasping
Machine Learning - Special issue on robot learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Using inaccurate models in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning for logistic regression: an evaluation
Machine Learning
Robotic Grasping of Novel Objects using Vision
International Journal of Robotics Research
A survey of robot learning from demonstration
Robotics and Autonomous Systems
Learning grasping affordances from local visual descriptors
DEVLRN '09 Proceedings of the 2009 IEEE 8th International Conference on Development and Learning
An analysis of active learning strategies for sequence labeling tasks
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Reducing labeling effort for structured prediction tasks
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-class ensemble-based active learning
ECML'06 Proceedings of the 17th European conference on Machine Learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Development of Object and Grasping Knowledge by Robot Exploration
IEEE Transactions on Autonomous Mental Development
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Visual features can help predict if a manipulation behavior will succeed at a given location. For example, the success of a behavior that flips light switches depends on the location of the switch. We present methods that enable a mobile manipulator to autonomously learn a function that takes an RGB image and a registered 3D point cloud as input and returns a 3D location at which a manipulation behavior is likely to succeed. With our methods, robots autonomously train a pair of support vector machine (SVM) classifiers by trying behaviors at locations in the world and observing the results. Our methods require a pair of manipulation behaviors that can change the state of the world between two sets (e.g., light switch up and light switch down), classifiers that detect when each behavior has been successful, and an initial hint as to where one of the behaviors will be successful. When given an image feature vector associated with a 3D location, a trained SVM predicts if the associated manipulation behavior will be successful at the 3D location. To evaluate our approach, we performed experiments with a PR2 robot from Willow Garage in a simulated home using behaviors that flip a light switch, push a rocker-type light switch, and operate a drawer. By using active learning, the robot efficiently learned SVMs that enabled it to consistently succeed at these tasks. After training, the robot also continued to learn in order to adapt in the event of failure.