[2506.16931] Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning

Computer Science > Artificial Intelligence arXiv:2506.16931 (cs) [Submitted on 20 Jun 2025 (v1), last revised 20 Mar 2026 (this version, v3)] Title:Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning Authors:Jiaqi Cheng, Mingfeng Fan, Xuefeng Zhang, Jingsong Liang, Yuhong Cao, Guohua Wu, Guillaume Adrien Sartoretti View a PDF of the paper titled Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning, by Jiaqi Cheng and 6 other authors View PDF Abstract:Effective and efficient task planning is essential for mobile robots, especially in applications like warehouse retrieval and environmental monitoring. These tasks often involve selecting one location from each of several target clusters, forming a Generalized Traveling Salesman Problem (GTSP) that remains challenging to solve both accurately and efficiently. To address this, we propose a Multimodal Fused Learning (MMFL) framework that leverages both graph and image-based representations to capture complementary aspects of the problem, and learns a policy capable of generating high-quality task planning schemes in real time. Specifically, we first introduce a coordinate-based image builder that transforms GTSP instances into spatially informative representations. We then design an adaptive resolution scaling strategy to enhance adaptability across different problem scales, and develop a multimodal fusion module with dedic...