Hierarchical RL

RL: Reinforcement Learning

Follow this topic Ask about this topic

Momentum

18 papers in the last four weeks, up 260% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 98

All topics

Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation
Jun 5, 2026Tuba Girgin, Jose Castelblanco, Gabriel Rodriguez +2Hierarchical RL Robotic Manipulation
Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA. · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Multi $^2$ : Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments
Jun 2, 2026Sangeun Park, Minhae KwonMulti-Agent LLM Systems Hierarchical RL
Department of Electrical and Computer Engineering, Sungkyunkwan University, Republic of Korea.
Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
May 27, 2026Kaiqiang Ke, Shenghong He, Chengdong Xu +3Hierarchical RL Offline RL
1Sun Yat-sen University · 2Pengcheng Laboratory
Exploiting Local Dynamics Regularity for Reusable Skills in Offline Hierarchical RL
May 25, 2026Sarthak Dayal, Abhinav Peri, Carl Qi +4Hierarchical RL Offline RL
Department of Computer Science University of Texas at Austin · UT Austin · OpenAI
Partner-Aware Hierarchical Skill Discovery for Robust Human-AI Collaboration
May 23, 2026Adnan Ahmad, Bahareh Nakisa, Mohammad Naim RastgooHierarchical RL Multi-Agent Collaboration
aSchoold of Information Technology, Deakin University, 75 Pidgons Rd, Waurn Ponds, 3216, VIC, Australia · bFaculty of Information Technology, Monash University, Wellington Rd, Clayton, 3800, VIC, Australia
Adaptive Human-AI Coordination via Hierarchical Action Disentanglement
May 23, 2026Adnan Ahmad, Bahareh Nakisa, Mohammad Naim RastgooDisentangled Representation Learning Hierarchical RL
School of Information Technology Deakin University, Waurn Ponds, 3216, VIC, Australia · Faculty of Information Technology, Monash University, 3800, VIC, Australia
Abstraction for Offline Goal-Conditioned Reinforcement Learning
May 21, 2026Clarisse Wibault, Alexander Goldie, Antonio Villares +2Hierarchical RL State Abstraction
FLAIR, MLRG · University of Oxford · FLAIR +1
Implicit Hierarchical GRPO: Decoupling Tool Invocation from Execution for Tool-Integrated Mathematical Reasoning
May 18, 2026Li Wang, Xiaohan Wang, Xiaodong Lu +5RL for Language Model Reasoning Hierarchical RL
Meituan · Tsinghua University
Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
May 16, 2026Vivienne Huiling Wang, Tinghuai Wang, Joni PajarinenWorld Model-Based Planning Hierarchical RL
Department of Electrical Engineering and Automation, Aalto University, Finland · Qutwo, Finland
Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models
May 16, 2026Siqi Luo, Jianghan Shen, Yi Xin +9Hierarchical RL Group Relative Policy Optimization
1Shanghai Jiao Tong University · 2Shanghai Artificial Intelligence Laboratory · 3Nanjing University +2
Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
May 15, 2026Dillon Z. Chen, Till Hofmann, Toryn Q. Klassen +1Long-Horizon Robotic Manipulation Symbolic Planning
1Vector Institute · University of Toronto · 3LAAS-CNRS +2
Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
May 14, 2026Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona +1Hierarchical RL Credit Assignment in RL
Sargent Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, UK · Department of Computer Science, University of Alberta, Edmonton, AB, Canada · Department of Computing, Imperial College London, London, SW7 2AZ, UK
Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents
May 13, 2026Xubo Lin, Zezhi Deng, Shihao Wang +2Hierarchical RL Conversational Agents
Georgetown University · Singapore Management University
Switching Successor Measures for Hierarchical Zero-shot Reinforcement Learning
May 13, 2026Stefan Stojanovic, Alexandre ProutiereRepresentation Learning Reinforcement Learning
KTH, Stockholm, Sweden · KTH, Digital Futures, Stockholm, Sweden
Delay-Empowered Causal Hierarchical Reinforcement Learning
May 12, 2026Chenran Zhao, Dianxi Shi, Haotian Wang +4Reinforcement Learning Hierarchical RL
College of Computer Science and Technology, National University of Defense Technology, Changsha, China · Intelligent Game and Decision Lab (IGDL), Beijing, China · School of Artificial Intelligence, Hebei University of Technology, Tianjin, China +1
Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
May 11, 2026Alberto Castagna, Stefan Zahlner, Adrian Egli +4Multi-Agent Coordination Intelligent Transportation Systems
∗enliteAI, AT · †SBB CFF FFS, CH · ‡Flatland Association, CH
Beyond Self-Play: Hierarchical Reasoning for Continuous Motion in Closed-Loop Traffic Simulation
May 9, 2026Weifan Zhang, Xiaofeng Zhao, Adel Bazzi +3Human Behavior Simulation Hierarchical RL
Isuzu Technical Center of America, Plymouth, MI, USA · School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA
Rethinking Priority Scheduling for Sequential Multi-Agent Decision Making in Stackelberg Games
May 8, 2026Xiangyu Liu, Liang Zhang, Bo Jin +1Sequential Decision Making Hierarchical RL
Dalian University of Technology
StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction
May 7, 2026Xiangyuan Xue, Yifan Zhou, Zidong Wang +5Agentic RL Hierarchical RL
1The Chinese University of Hong Kong · 2Shanghai Artificial Intelligence Laboratory · University of Georgia +2
Hierarchical Multiagent Reinforcement Learning for Multi-Group Tax Game
May 6, 2026Honglei Guo, Yexin Li, Chiyuan Wang +1Game Theory Hierarchical RL
College of Computer Science and Technology Zhejiang University State Key Laboratory of General Artificial Intelligence, BIGAI
Quantum Hierarchical Reinforcement Learning via Variational Quantum Circuits
May 5, 2026Yu-Ting Lee, Samuel Yen-Chi Chen, Fu-Chieh ChangHierarchical RL Quantum Machine Learning
Graduate Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan · Wells Fargo, New York, NY, USA · MediaTek Inc., Hsinchu, Taiwan
MARS-DA: A Hierarchical Reinforcement Learning Framework for Risk-Aware Multi-Agent Bidding in Power Grids
May 4, 2026Jiayi Chen, Xuan Zhang, Guiling WangHierarchical RL Risk-Sensitive RL
Department of Computer Science, New Jersey Institute of Technology
Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
May 3, 2026Polydoros Giannouris, Yuechen Jiang, Lingfei Qian +5Reinforcement Learning Hierarchical RL
Bi-Level Reinforcement Learning Control for an Underactuated Blimp via Center-of-Mass Reconfiguration
May 2, 2026Xiaorui Wang, Hongwu Wang, Yue Fan +2RL Control Aerial Robotics
Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training
Apr 29, 2026Mahya Ramezani, Holger VoosReinforcement Learning UAV Navigation
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg.
Hierarchical Behaviour Spaces
Apr 27, 2026Michael Tryfan Matthews, Anssi Kanervisto, Jakob Foerster +3Reinforcement Learning Hierarchical RL
Meta Superintelligence Labs · University of Oxford
Unleashing the Agility of Wheeled-Legged Robots for High-Dynamic Reflexive Obstacle Evasion
Apr 26, 2026Yongen Zhao, Zihao Xu, Wenzhi Lu +4Hierarchical RL Robot Locomotion
Beijing Zhongguancun Academy, Beijing, China · School of Mechanical Engineering, Tianjin University, Tianjin, China · School of Computing, National University of Singapore, Singapore +1
Hierarchical Active Inference using Successor Representations
Apr 17, 2026Prashant Rangarajan, Rajesh P. N. RaoActive Inference Hierarchical RL
Paul G. Allen Center for Computer Science and Engineering, University of Washington, Seattle, USA
Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors
Apr 6, 2026Hieu Le, Mostafa Ibrahim, Oguz Bedir +2Reconfigurable Intelligent Surfaces Hierarchical RL
Electrical and Computer Engineering Texas A&M University College Station, Texas, USA · Engineering Technology and Industrial Distribution Texas A&M University College Station, Texas, USA · School of Performance, Visualization, and Fine Arts Texas A&M University College Station, Texas, USA
Chain-of-Goals Hierarchical Policy for Long-Horizon Offline Goal-Conditioned RL
Feb 3, 2026Jinwoo Choi, Sang-Hyun Lee, Seung-Woo SeoReinforcement Learning Hierarchical RL
Department of Electrical and Computer Engineering, Seoul National University, Seoul, South Korea · Department of Automotive Engineering, Ajou University, Gyeonggi-do, South Korea

Jun 5, 2026, cs.ROJ/K move · Enter open · S save

Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation

Tuba Girgin, Jose Castelblanco, Gabriel Rodriguez, Emre Girgin+1

Department of Aerospace Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, USA. · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.

Hierarchical RL Robotic Manipulation Robot Learning

Open paper AskPDF arXiv

The object manipulation capabilities of quadruped robots is an open research challenge. While previous studies have focused on low-level policy learning, task execution still relies on expert-designed high-level trajectories. Autonomous selection of both an affordable interaction point on the target object and an affordable robot base pose removes the need for pre-designed trajectories. This study proposes a three-level hierarchical reinforcement learning (RL) framework that utilizes pose affordances to guide the navigation policy, while the navigation policy drives the locomotion policy. In addition, the pedipulation policy is guided by interaction-point affordances, enabling object-centric pose alignment of the quadruped robot and effective end-effector manipulation planning. We train the proposed framework in the IsaacSim ecosystem and evaluate it in both simulation and real-world settings. We investigate the effectiveness of pose affordance across multiple scenarios in simulation while various object interaction tasks are validated on real-world setting forming an object-interaction dataset. The results show that the proposed framework can autonomously identify candidate poses based on their affordance and successfully execute object manipulation tasks in the real world without human guidance.

Momentum

Related topics

Latest papers 98