Integrating Algorithmic Planning and Deep Learning for Partially Observable Navigation
This addresses robot navigation challenges for robotics applications, but it appears incremental as it combines existing model-free and model-based methods in a new framework.
The authors tackled robot navigation in unseen 3-D environments with partial observability by integrating algorithmic planning and deep learning into a single end-to-end trainable recurrent neural network called Navigation Networks (NavNets), and they successfully trained these networks to solve the task in preliminary simulations.
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. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.