Hierarchical RL

RL: Reinforcement Learning

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

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Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation
Jan 16, 2026Zhixian Xie, Yu Xiang, Michael Posa +1Contact-Rich Manipulation Non-Prehensile Manipulation
Arizona State University · University of Texas at Dallas · University of Pennsylvania
Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning
Sep 22, 2025Chuhao Qin, Evangelos PournarasMulti-Agent System Optimization Multi-Agent Coordination
School of Computer Science, University of Leeds, UK
Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning
Jul 31, 2025Tommaso Marzi, Cesare Alippi, Andrea CiniMulti-Agent Coordination Hierarchical RL
IDSIA USI-SUPSI, Università della Svizzera italiana, Lugano, Switzerland · Politecnico di Milano, Milan, Italy · EPFL, Lausanne, Switzerland
Action abstractions for amortized sampling
Oct 19, 2024Oussama Boussif, Léna Néhale Ezzine, Joseph D Viviano +6Hierarchical RL Generative Flow Networks
Mila – Qu´ebec AI Institute · Universit´e de Montr´eal · University of Toronto +3
Hierarchical Deep Counterfactual Regret Minimization
May 27, 2023Jiayu Chen, Xudong Wu, Zhekai Wang +1Imperfect-Information Games Hierarchical RL
The University of Hong Kong Hong Kong SAR · Peking University Beijing, China, 100871 · Purdue University2025 West Lafayette, IN 47907
Diversity-Enriched Option-Critic
Nov 4, 2020Anand Kamat, Doina PrecupHierarchical RL Intrinsic Motivation
McGill University · Mila · 3Google deepmind
Discovering Temporal Structure: An Overview of Hierarchical Reinforcement Learning
Date pendingMartin Klissarov, Akhil Bagaria, Ziyan Luo +3Hierarchical RL
Mila, McGill University · Amazon · Amii, University of Alberta Canada CIFAR AI Chair
CoSkill: Joint Reinforcement Learning of Reasoning and Meta-Skill Agents for Hierarchical Skill Evolution
Date pendingJinyuan Feng, Dongmin Li, Yiqun Chen +4Hierarchical RL LLM Agent Skill Learning
Institute of Automation, Chinese Academy of Sciences · Renmin University of China · ByteDance

Jan 16, 2026, cs.ROJ/K move · Enter open · S save

Where to Touch, How to Contact: A Hierarchical RL-MPC Framework for Geometry-Aware Sim-to-Real Manipulation

Zhixian Xie, Yu Xiang, Michael Posa, Wanxin Jin

Arizona State University · University of Texas at Dallas · University of Pennsylvania

Contact-Rich Manipulation Non-Prehensile Manipulation Contact-Rich Robotic Manipulation Sim-to-Real Transfer Hierarchical RL Generalization in Robotic Manipulation+2

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A key challenge in contact-rich dexterous manipulation is the need to jointly reason over global geometry and nonsmooth contact dynamics. End-to-end policies bypass this complexity, but often require large amounts of data and transfer poorly from simulation to reality. We address the limitations with a simple insight: dexterous manipulation is inherently hierarchical--at a high level, a robot decides where to touch (geometry); at a low level it determines how to move the object through contact dynamics. Building on this insight, we propose a hierarchical RL--MPC framework in which a high-level reinforcement learning (RL) policy predicts a contact intention, a novel object-centric interface that specifies (i) an object-surface contact location and (ii) a post-contact object subgoal pose. Conditioned on the contact intention, a low-level contact-implicit model predictive control (MPC) optimizes local contact modes and real-time (re)plans through contact dynamics to generate robot actions that robustly move the object toward each subgoal. We evaluate the framework on non-prehensile tasks, including geometry-generalized pushing across diverse object shapes, pivoting/flipping-based object reorientation, and environment-assisted object repositioning. It achieves high success rate with substantially reduced data (10 times less than end-to-end baselines), highly robust performance, and zero-shot sim-to-real transfer.

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