{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-sketch-guided-path-planning","title":"End-to-end Sketch-Guided Path Planning through Imitation Learning for Autonomous Mobile Robots","arxiv_id":null,"date":"2025-03-21","proceeding":"IEEE 6th International Conference on Image Processing, Applications and Systems (IPAS) 2025 3","authors":["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.","url_abs":"https://ieeexplore.ieee.org/document/10924509","url_pdf":"https://ieeexplore.ieee.org/document/10924509","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-sketch-guided-path-planning","repo_url":"https://github.com/charbel-a-hC/SKIPP","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"imitation-learning","task_name":"Imitation Learning"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}