The handbook of brain theory and neural networks
Advanced Robotics: Redundancy and Optimization
Advanced Robotics: Redundancy and Optimization
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
TITLE A Hybrid Discrete Event Dynamic Systems Approach to Robot Control
TITLE A Hybrid Discrete Event Dynamic Systems Approach to Robot Control
A hybrid architecture for adaptive robot control
A hybrid architecture for adaptive robot control
Visual feature learning
Multifingered grasping: grasp reflexes and control context
Multifingered grasping: grasp reflexes and control context
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dependency Networks for Relational Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
The initial development of object knowledge by a learning robot
Robotics and Autonomous Systems
Autonomous development of a grounded object ontology by a learning robot
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Goal emulation and planning in perceptual space using learned affordances
Robotics and Autonomous Systems
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This paper proposes a methodology for learning joint probability estimates regarding the effect of sensorimotor features on the predicated quality of desired behavior. These relationships can then be used to choose actions that will most likely produce success. relational dependency networks are used to learn statistical models of procedural task knowledge. An example task expert for picking up objects is learned through actual experience with a humanoid robot. We believe that this approach is widely applicable and has great potential to allow a robot to autonomously determine which features in the world are salient and should be used to recommend policy for action.