Graph-Based Planning

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Overview

Graph-based planning represents places, actions, task dependencies, or possible system states as nodes and connections, then searches the graph for a sequence that meets goals and constraints. Classical approaches include A*, Dijkstra, hybrid and any-angle search, and optimization over graphs of convex sets; they are used in robot navigation, multi-agent pathfinding, and task execution. Newer methods add sparse or topological maps, learned heuristics and graph neural networks, world models, and task graphs for language-model agents. A central challenge is balancing speed and scalability with optimality, safety, and reliable behavior when maps or conditions change.

Latest papers

CardsList

99 papers15 papers in the last four weeks, up 7% on the four weeks before. 0.1% of all new papers.

  1. Two-Stage Path Following for Mobile Manipulators via Dimensionality-Reduced Graph Search and Numerical Optimization

    Mar 7, 2026Fuyu Guo, Yuting Mei, Yuyao Zhang +1Robot Motion PlanningConstrained Motion Planning

    College of Mechanical and Vehicle Engineering, ChongQing University, Chongqing, P.R.C.

  2. ViterbiPlanNet: Injecting Procedural Knowledge via Differentiable Viterbi for Planning in Instructional Videos

    Mar 4, 2026Luigi Seminara, Davide Moltisanti, Antonino FurnariGraph-Based PlanningTemporal Video Understanding

    University of Catania · University of Bath

  3. CASSR: Continuous A-Star Search through Reachability for real time footstep planning

    Mar 3, 2026Jiayi Wang, Steve TonneauMotion PlanningConstrained Motion Planning

    State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing 100080, China · University of Edinburgh, IPAB, Edinburgh EH8 9YL, U.K.

  4. Learning Shortest Paths with Generative Flow Networks

    Mar 2, 2026Nikita Morozov, Ian Maksimov, Daniil Tiapkin +1Graph-Based PlanningGenerative Flow Networks

    HSE University · CMAP, CNRS, École polytechnique · LMO, Université Paris-Saclay

  5. UPath: Universal Planner Across Topological Heterogeneity For Grid-Based Pathfinding

    Feb 27, 2026Aleksandr Ananikian, Daniil Drozdov, Konstantin YakovlevMotion PlanningOOD Generalization

    Saint-Petersburg University

  6. SCOPE: Skeleton Graph-Based Computation-Efficient Framework for Autonomous UAV Exploration

    Feb 26, 2026Kai Li, Shengtao Zheng, Linkun Xiu +4Autonomous Robot ExplorationRobot Navigation

    Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639956

  7. ProAct: A Benchmark and Multimodal Framework for Structure-Aware Proactive Response

    Feb 3, 2026Xiaomeng Zhu, Fengming Zhu, Weijie Zhou +8Proactive AgentsAI Agent Benchmarks

    Department of Computer Science and Engineering, The Hong Kong University of Science and Technology (HKUST), Hong Kong SAR, China · Tencent, Shenzhen, China · Futian Laboratory, Shenzhen, China +1

  8. STITCHER: Constrained Trajectory Planning in Complex Environments with Real-Time Motion Primitive Search

    Oct 16, 2025Helene J. Levy, Brett T. LopezConstrained Motion PlanningTrajectory Generation

    VECTR Laboratory, University of California, Los Angeles, Los Angeles, CA, USA

  9. Path Planning on Multi-level Point Cloud with a Weighted Traversability Graph

    Apr 30, 2025Yujie Tang, Quan Li, Hao Geng +3Motion PlanningOff-Road Robot Navigation

    Areaspace Information Research Institute, Chinese Academy of Sciences, Beijing, China · School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, 200444, China · Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, 200444, China