Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation

Understand

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e.

• a deep neural network.
• This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods.
• We apply the proposed approach to a challenging partially observable robot navigation task.