Towards a human-inspired control architecture for visually-guided action

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Despite the increasing sophistication of robotic systems, humans remain superior to robots in an impressive number of ways - not least when it comes to acting in natural environments. The proposed project aims at understanding a set of basic processes that are crucial for such interactions, with this understanding tested through the development of a computer model that is evaluated relative to human behaviour. The process to be modelled concern our ability to shape an action to a task-relevant object present in a complex (multi-object) environment. The proposed model is based on the premise that successful human actions depend on two forms of information: the abstract function of the objects (this is something to cut with) and the visual properties of the objects that are associated with action (that the handle but not the blade can be grasped). Psychological evidence also indicates that the intended action for a given task can influence how human attention is directed. Selection of the object to act upon is not separated from the selection of which action to make. To capture this, the selection of objects and the selection of actions will be modelled as interactive process.Prior work by the applicant provides a promising backdrop to the project. In particular, a model of object selection has been generated (the SAIM model) which has been successfully applied to a large body of psychological data. A second model has simulated how actions are select to single objects (the NAM model). In this project the models will be merged together, to simulate interactions between perceptual selection and action. The model will be realised using a neural-like architecture, and tested in comparison to human data. In the longer term, the model will provide an architecture for real-time control of a robot arm operating in a complex environment