Bioinspired robotic vision with online learning capability and rotation-invariant properties

Abstract

Reliable image perception is critical for living organisms. Biologic sensory organs and nervous systems evolved interdependently to allow apprehension of visual information regardless of spatial orientation. By contrast, convolutional neural networks usually have limited tolerance to rotational transformations. There are software-based approaches used to address this issue, such as artificial rotation of training data or preliminary image processing. However, these workarounds require a large computational effort and are mostly done offline. This work presents a bioinspired, robotic vision system with inherent rotation-invariant properties that may be taught either offline or in real time by feeding back error indications. It is successfully trained to counter the move of a human player in a game of Paper Scissors Stone. The architecture and operation principles are first discussed alongside the experimental setup. This is followed by performance analysis of pattern recognition under misaligned and rotated conditions. Finally, the process of online, supervised learning is demonstrated and analyzed.