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At the same time, I think it's quite far from those references, as this simple vibratory activation of the place involves an active perception of the site, developing attention to all the senses.
We consider an active perception system, consisting of a camera mounted on a pan-tilt unit and a 360° RFID detection system, both embedded on a mobile robot.
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As applied to imagery (Thomas, 1999b), this appears to be consistent with Paivio's characterization of it as "a dynamic process more like active perception than a passive recorder of experience" (Paivio, 1977).
Actionability also somewhat changes the traditional concerns of affordances to suggest a greater emphasis on active perception.
This paper presents a model for the autonomous learning of smooth pursuit eye movements based on an efficient coding criterion for active perception.
We examine the problem of deciding when, how and where the electronic nose (e-nose) should be activated by planning for active perception and we consider the problem of integrating the information provided by the e-nose with both prior information and information from other sensors (e.g., vision).
The approach is novel in that it exploits the ability of a robot to learn through active perception in the task environment, similar to the learning by experimentation technique developed for LEX (Mitchell et al., 1990).
In fact, we see little published prior art on these key points made in our paper: A) Our measurements are in defined neurons in an active sensory perception task.
It will also provide a ground-truth simulation environment to evaluate potential active perception strategies.
We are building a robot cognitive architecture that is based on a unified cognitive architecture - Soar - and that uses active perception and simulation in planning.
Our results are consistent with the hypothesis that the cerebellum is vital to sensory prediction of self-generated movement and suggest a general role for the cerebellum in multiple forms of active perception.
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