GAN Path Finder: Preliminary results
This work proposes a novel method for a well-known robotics or AI planning problem, but it is incremental as it adapts existing deep learning techniques to a new application.
The authors tackled 2D path planning in static environments by treating it as an image generation task using a generative neural network, reporting preliminary results that suggest this approach is promising for further research.
2D path planning in static environment is a well-known problem and one of the common ways to solve it is to 1) represent the environment as a grid and 2) perform a heuristic search for a path on it. At the same time 2D grid resembles much a digital image, thus an appealing idea comes to being -- to treat the problem as an image generation task and to solve it utilizing the recent advances in deep learning. In this work we make an attempt to apply a generative neural network as a path finder and report preliminary results, convincing enough to claim that this direction of research is worth further exploration.