Learning Responses to Visual Stimuli: A Generic Approach

  • Liam Ellis
  • Richard Bowden
Schlagworte: autonomous, control, generic-problem-solving, DDC: 004 (Data processing, computer science, computer systems)


A general framework for learning to respond appropriately to visual stimulus is presented. By hierarchically clustering percept-action exemplars in the action space, contextually important features and relationships in the perceptual input space are identified and associated with response models of varying generality. Searching the hierarchy for a set of best matching percept models yields a set of action models with likelihoods. By posing the problem as one of cost surface optimisation in a probabilistic framework, a particle filter inspired forward exploration algorithm is employed to select actions from multiple hypotheses that move the system toward a goal state and to escape from local minima. The system is quantitatively and qualitatively evaluated in both a simulated shape sorter puzzle and a real-world autonomous navigation domain.
The 5th International Conference on Computer Vision Systems