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IPAB PhD Thesis proposals

Student Date  First Year Proposal Title 
Cigdem Beyan 04/12 An Abnormal Behaviour Detection Method for Fish Behaviour Understanding
Hsiu-Chin Lin 03/12 Learning Representations for Quantifying Periodic Gaits and Transitions
Simon Smith Bize 02/12 Self-Organisation of Internal Models in Autonomous Robots
Alexander Enoch 09/11  Biololgically inspired robotic hardware and control methods for efficient dynamic bipedal locomotion
Xuan Huang 09/11  That's how machine learns fish (Computer Vision & Machine Learning In fish species recognition
Joseph Henry 09/11 Multi-agent motion and interaction for computer animation 
Xi Zhao 09/11  Spatial relationship based motion analysis and synthesis 
Luigi Acerbi  09/11 Inferring the structure of space-time through action and perception
Peter Sandilands  08/11  Automatic Generation of Interactions Involving the Human Hand 
Vladimir Ivan 07/11 Approximate probabilistic inference for motion planning in topology based task spaces 
Benjamin Rosman 09/10 Intelligent decision-making, acting in large worlds, strategic agent interactions. 
Evelina Overlingaite 09/10 Optimality in Sensorimotor Control - theory and human experiments 
Steven McDonagh 09/10  3D Shape Fusion and Modelling with large sets of range views
Aris Valtazanos 05/10 Design and online learning of strategic behaviours in the robotic soccer domain
Chris Towell 11/10 Learning force control policies from constrained human demonstrations 
Majd Hawasly 10/09 A Framework for Robust Multi-Robot Strategic Decision-Making 
Konrad Rawlik 09/09 Stochastic optimal control, Bayesian models, human variable impedance control
Adam Barnett 06/09  Using graphs for producing scenes of teamwork in graphics
Georgios Petrou  05/09 A biorobotic investigation of phonotactic turning in crickets
He Wang 05/09  Topological based control 
Lily Li 08/08  Using colour and topographic information for skin lesion diagnosis and analysis 
Matthew Whitaker  06/07  Investigating the effects of inter-agent communication on multiagent reinforcement learning 
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