End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile Robots

Anthony Rizk, Charbel Abi Hana, Youssef Bakouny, Flavia Khatounian

Abstract: Path planning is crucial for Autonomous Mobile Robots applications. Traditionally, path planning based on human input and preferences has relied on hard to define reward-based learning or costly techniques requiring additional hardware. This work introduces a more accessible and flexible approach through sketch-guided imitation learning, where nontechnical users can simply draw the desired navigational path on a provided 2D map, which is then used to teach U-net models path planning behaviors. Additionally, the work draws on metrics from the fields of image generation and robotics to provide a novel evaluation framework. The approach is integrated into an end-to-end robotics stack to demonstrate its usability. The dataset and code are provided on https://github.com/charbel-a-hC/SKIPP.

External IDs:dblp:conf/ipas2/RizkRHBK25