Potentials and Limitations of Deep Neural Networks for Cognitive Robots
This work highlights limitations in applying deep neural networks to cognitive robotics, suggesting alternative methods for researchers in robotics and AI.
The paper argues that while deep neural networks are effective for perceptual tasks like audio and vision in cognitive robots, they fall short in addressing broader cognitive challenges, and proposes reservoir computing as a promising approach for sequential learning in this domain.
Although Deep Neural Networks reached remarkable performance on several benchmarks and even gained scientific publicity, they are not able to address the concept of cognition as a whole. In this paper, we argue that those architectures are potentially interesting for cognitive robots regarding their perceptual representation power for audio and vision data. Then, we identify crucial settings for cognitive robotics where deep neural networks have as yet only contributed little compared to the challenges in cognitive robotics. Finally, we argue that the rather unexplored area of Reservoir Computing qualifies to be an integral part of sequential learning in this context.