Jeremy Wyatt


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PhD projects

Jeremy Wyatt is interested in learning in robots and reinforcement learning. Specific projects include: Reinforcement learning implementations on real robots Experimental design and analysis in robot learning Exploration control in reinforcement learning Reinforcement learning with dynamic Bayes networks Evolution of perceptual processing Task-driven hidden Markov modelling Learning in neural network ensembles


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Personal profile

Research interests

I'm interested in a number of problems, all of which are motivated by the same scientific goal:studying general architectures and methods for learning and reasoning in autonomous agents, especially those with bodies. My interests are broad. I have worked on the exploration-exploitation problem in reinforcement learning, the problem of managing diversity in committees of learning machines, cognitive architectures for intelligent robotics, learning of predictions in robot manipulation, planning and learning of information gathering strategies in robots (e.g. in AUVs, in processing of images, in gaze control, or in object search), and on the use of physics knowledge in prediction and estimation in vision. There are relevant publications on these topics and many others below. My current interests are all based around the need for robots to understand their surroundings, and to be able to extend that understanding by themselves, and with others.


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