Hierarchical Planning

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Hierarchical planning studies how to break complex tasks into subgoals at multiple levels so agents can plan farther ahead without searching every possible action sequence. Methods include symbolic Hierarchical Task Networks, reusable skills or options, learned latent subgoals and world models, and language models that propose or refine plans. Researchers evaluate these approaches in settings such as robot navigation and manipulation, household and web agents, multi-robot coordination, and scheduling. A central challenge is choosing useful levels of detail while ensuring abstract plans remain feasible in the real environment and can recover when execution goes wrong.

Latest papers

35 papers7 papers in the last four weeks, up 40% on the four weeks before. 0.0% of all new papers.

Hierarchical Task Network planning in the Goal Lifecycle
Oct 26, 2026Paola Rizzo, Hector Munoz-Avila, David W. AhaDynamic Replanning Hierarchical Planning
Interagens s.r.l., Rome, Italy · Lehigh University, Bethlehem, PA, USA · Navy Center for Applied Research in AI; Naval Research Laboratory, Washington DC, USA
Collective Collaboration of Recursive Autonomous Agents
Oct 24, 2026Ziqing Zhou, Linqiang Hu, Yuning Chen +4Multi-Agent Collaboration Multi-Agent Systems
WorldStream: Streaming Interactive 3D Worlds with Hierarchical Planning and Persistent State
Oct 20, 2026Xiaoxiao Ma, Weiyang Jin, Yanwen Ma +11Interactive World Models 3D Scene Generation
University of Science and Technology of China · Joy Future Academy, JD
HTN-Orchestrate: A Hierarchical Task Network Framework for Multi-Agent Orchestration
Oct 15, 2026Yanchen Deng, Jianguo Huang, Yao Long Teng +6Multi-Agent Orchestration LLM Agent Orchestration
Nanyang Technological University, Singapore · Apodex.
ATOM: Abstract Transition and Option Modeling - Experience-Informed Hierarchical Navigation
Oct 13, 2026Changxuan Yao, Panagiotis TsiotrasRobot Navigation Hierarchical Planning
Institute for Robotics and Intelligent Machines Georgia Institute of Technology, Atlanta, GA
SCAD: Structured Credit Assignment and Distillation for Long-Horizon Agents
Oct 2, 2026Shangyang Wu, Shuai Zhao, Ziyue Zhu +3Hierarchical Planning Step-Level Credit Assignment in RL
Beijing University of Posts and Telecommunications · Tsinghua University
Local-Minimum Escaper: Programmatic Subgoal Generation for Robust Navigation in Unknown Environments
Sep 30, 2026Yin Gu, Xinming Zhang, Shanze Wang +2Hierarchical Planning Robot Navigation
Eastern Institute of Technology, Ningbo, China · Tsinghua University, China · Department of Aeronautical and Aviation Engineering, Polytechnic University, Hong Kong
HorizonFlow: Variable-Length Planning for Offline Goal-Conditioned RL
Sep 29, 2026JunHyeok Oh, Zian Jang, Byung-Jun LeeHierarchical Planning Trajectory Generation
Korea University. · Gauss Labs Inc., Seoul, Republic of Korea.
Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals
Sep 25, 2026Royson Lee, Fady Rezk, Titouan Parcollet +2Long-Horizon Planning Goal-Conditioned Planning
Samsung AI, Cambridge, UK · University of Edinburgh, UK
HOPHY: A Hierarchical Hypergraph Representation for Off-Road Path and Mission Planning
Sep 17, 2026Pranay Meshram, Charuvahan Adhivarahan, Prithvi Poddar +4Hierarchical Planning Graph-Based Planning
Department of Computer Science and Engineering, University at Buffalo, NY 14260, USA. · Department of Mechanical and Aerospace Engineering, University at Buffalo, NY 14260, USA.
Towards Numerical TOHTN Planning with SMT-based HTN-SAT Encoding
Sep 3, 2026Gaspard Quenard, Takudzwa Togarepi, Damien Pellier +1Hierarchical Planning Symbolic Planning
1Univ. Grenoble Alpes, France
Lose the Order, Keep the Hierarchy: Deordering HTN Plans
Sep 3, 2026Takudzwa Togarepi, Gaspard Quenard, Damien Pellier +1Hierarchical Planning Symbolic Planning
1Universit´e Grenoble Aples, France
RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolution
Sep 2, 2026Zijian Zhao, Sen Li, Xialiang Tong +1Vehicle Routing Hierarchical Planning
The Hong Kong University of Science and Technology · Noah’s Ark Lab, Huawei · The Hong Kong University of Science and Technology (Guangzhou)
Toward User-Mediated Self-Repair in Ubiquitous Robots Through Goal-Oriented Agentic AI
Aug 11, 2026Morten Roed FrederiksenAgentic AI Human-Robot Interaction
Data Systems and Robotics (DSAR) Department of The IT-University of Copenhagen Denmark
Complete Motion Planning using Workspace-Fibered Decomposition for nR-Planar Manipulator
Aug 2, 2026Aayush Rath, Antony ThomasConstrained Motion Planning Hierarchical Planning
Robotics Research Center, IIIT Hyderabad, Hyderabad 500032, India.
ReflectVLN: Training Vision-Language Navigation Agents with Reflective Reasoning
Jul 14, 2026Jiahang Wang, Yirong Yang, Yanqing Zhu +4Hierarchical Planning Vision-Language Navigation
Amap, Alibaba Group, China · Beihang University
Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel
Jul 14, 2026Niccolò Caselli, Francesco Massafra, Samuele Punzo +3Hierarchical Planning Long-Horizon Robotic Manipulation
University of Amsterdam
TerraLogic: A Benchmark for Hierarchical Geospatial Reasoning in Earth Observation
Jul 14, 2026Yuhang Yan, Linchao Mou, Bokang Yang +1Spatial Reasoning Hierarchical Planning
The Chinese University of Hong Kong (Shenzhen) China · Technical University of Munich Germany
AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
Jul 3, 2026Qi Liu, Yabei Li, Hongsong Wang +2Hierarchical Planning Vision-Language-Action Models
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China · School of Computer Science and Engineering, Southeast University, Nanjing 210096, China +2
How Should Agents Read Demonstrations? Hierarchical Structure Beats Flat Action Logs
Jun 18, 2026Honjar Xing, Jefferson Lin, Henry LiebermanHierarchical Planning Learning from Demonstration
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA.
Beyond Global Replanning: Hierarchical Recovery for Cross-Device Agent Systems
Jun 18, 2026Shu Yao, Yuhua Luo, Qian Long +7Hierarchical Planning Multi-Agent Orchestration
School of Artificial Intelligence, Shanghai Jiao Tong University · 2Shanghai Innovation Institute · 3Southeast University +1
FF-JEPA: Long-Horizon Planning in World Models with Latent Planners
Jun 8, 2026Sergi Masip, Jonathan Swinnen, Yutong Hu +2Hierarchical Planning Long-Horizon Robotic Manipulation
PSI, ESAT, KU Leuven · RAM, MECH, KU Leuven
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Jun 4, 2026Ziyang Yao, Haochen Liu, Yuncheng Jiang +10Hierarchical Planning World Model Learning
Xiaomi EV
Deconstructing Spatial Complexity: Hierarchical Decomposition for LLM Spatial Reasoning
May 27, 2026Yi Wang, Haojie Lu, Zhaofan Zhang +2Hierarchical Planning RL for Language Model Reasoning
The Hong Kong University of Science and Technology (Guangzhou)
Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement
May 19, 2026Isaac Ngui, Courtney McBeth, James D. Motes +2Multi-Robot Motion Planning Hierarchical Planning
University of Illinois Urbana-Champaign, Urbana IL 61801, USA · Instituto Tecnológico Autónomo de México, Mexico City, Mexico
HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks
May 9, 2026Qingyuan Luo, Jie Li, Meng GuoMulti-Agent Coordination Hierarchical Planning
the Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing 100871, China · National University of Defense Technology, Hunan 410073, China
Hierarchical Task Network Planning with LLM-Generated Heuristics
May 8, 2026Felipe Meneguzzi, Alexandre Buchweitz, Augusto B. Corrêa +2Language Model-Guided Search Heuristics Hierarchical Planning
University of Aberdeen, UK · University of Oxford, UK · PUCRS, Brazil +2
HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
May 6, 2026Nandiraju Gireesh, Yuanliang Ju, Chaoyi Xu +3Hierarchical Planning Long-Horizon Robotic Manipulation
1Peking University · University of Toronto. · 2Galbot
Bridging Values and Behavior: A Hierarchical Framework for Proactive Embodied Agents
Apr 30, 2026Chunhui Zhang, Yuxuan Wang, Aoyang Qin +4Hierarchical Planning Agent Evaluation
State Key Laboratory of General Artificial Intelligence, BIGAI · School of Computer Science, Peking University · Tsinghua University +3
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
Apr 25, 2026Haoran Tan, Zeyu Zhang, Chen Ma +3Hierarchical Planning Long-Horizon Agent Tasks
Gaoling School of Artificial, Renmin University of China, Beijing, China · Huawei Noah’s Ark Lab

Oct 26, 2026J/K move · Enter open · S save

Hierarchical Task Network planning in the Goal Lifecycle

Paola Rizzo, Hector Munoz-Avila, David W. Aha

Interagens s.r.l., Rome, Italy · Lehigh University, Bethlehem, PA, USA · Navy Center for Applied Research in AI; Naval Research Laboratory, Washington DC, USA

Dynamic Replanning Hierarchical Planning

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Goal reasoning (GR) is the process by which intelligent agents continually reason about the goals they are pursuing, which may lead to goal change, for instance when opportunities arise, or when events cause discrepancies among expected and actual outcomes of actions planned to achieve goals. In this paper we investigate whether Hierarchical Task Network (HTN) planning can provide a uniform implementation of the Goal Lifecycle, a flexible process model for goal reasoning in dynamic environments. The Goal Lifecycle progressively refines goals by adding constraints to them through a series of strategies (e.g., {\it formulate, select, expand}) and uses other strategies (e.g., {\it defer, re-expand, repair}) to resolve the current goal when an event impacts it. We present a novel GR agent architecture that employs HTN planning in this Goal Lifecycle. In addition to using HTN planning for the expand and re-expand strategies, as is frequently done in GR and planning research, we also use it in the formulate strategy, to obtain a uniform planning approach that provides several benefits (e.g., the possibility to formulate new goals in view of achieving higher level overarching goals). We also explain how we use an enhanced HTN action representation to process discrepancies among expected and actual action outcomes that can trigger the transition from one goal to another. Finally, we empirically demonstrate that our GR architecture enables simulated autonomous vehicles to adaptively conduct missions in dynamic environments.