CAVIAR Project

Technical Approach

CAVIAR will develop the modules for assembling a vision system demonstrator that goes from the sensor, through programmable bio-inspired processing layers, to provide outputs for use in actuation. A long term application goal of CAVIAR is to use the results in “Automatic Car Driving”. However, for the purpose of the present proposal, the CAVIAR consortium proposes to assemble a little demonstrator which would be “a small robot following a ball”. The robot, equipped with a sensing retina, a set of low level convolutional processing filters and high level processing stages, segments the sensed images, identifies the ball and gives instructions to center the ball on the retina. Adaptation is also part of CAVIAR, since learning stages are included in the high-level processing layers. This set of modules comprises (see Fig. 2)

• A front-end vision sensor that consists of a retina that outputs irradiances changes in time in the AER-coded form.
• A general purpose programmable-kernel convolution module to implement projection fields from layer to
layer. This module exploits the use of a special in-chip pulse width modulation technique, which allows expansion to a multi-chip structure within the same processing layer.
• A dimension-reduction competition module that uses a reduced-dimension space to represent the low-level features extracted by the convolution system, while making salient features to compete.
• A learning module that implements a spike-based learning rule that is amenable to the incoming AER inputs. The learning will be based on spatio-temporal statistics of the inputs.

These modules are all bound together by the AER infrastructure. All communication between the modules (including bidirectional communication) follows the AER protocol.

Demonstrator: To test the integrity of this perceptive-action vision system using the AER infrastructure, we will mount this system on a commercial robot and use the robot in a reference task.

Project Main Goals
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Technical Approach
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