  Path planning is a fundamental capability for autonomous navigation of
robotic wheelchairs. With the impressive development of deep-learning
technologies, imitation learning-based path planning approaches have achieved
effective results in recent years. However, the disadvantages of these
approaches are twofold: 1) they may need extensive time and labor to record
expert demonstrations as training data; and 2) existing approaches could only
receive high-level commands, such as turning left/right. These commands could
be less sufficient for the navigation of mobile robots (e.g., robotic
wheelchairs), which usually require exact poses of goals. We contribute a
solution to this problem by proposing S2P2, a self-supervised goal-directed
path planning approach. Specifically, we develop a pipeline to automatically
generate planned path labels given as input RGB-D images and poses of goals.
Then, we present a best-fit regression plane loss to train our data-driven path
planning model based on the generated labels. Our S2P2 does not need pre-built
maps, but it can be integrated into existing map-based navigation systems
through our framework. Experimental results show that our S2P2 outperforms
traditional path planning algorithms, and increases the robustness of existing
map-based navigation systems. Our project page is available at
https://sites.google.com/view/s2p2.
