Neural Network Memory Architectures for Autonomous Robot Navigation

Understand

This paper highlights the significance of including memory structures in neural networks when the latter are used to learn perception-action loops for autonomous robot navigation.

• Traditional navigation approaches rely on global maps of the environment to overcome cul-de-sacs and plan feasible motions.
• Yet, maintaining an accurate global map may be challenging in real-world settings.
• A possible way to mitigate this limitation is to use learning techniques that forgo hand-engineered map representations and infer appropriate control responses directly from sensed information.