RL for Robotics

RL: Reinforcement Learning

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31 papers in the last four weeks, up 675% on the four weeks before. 0.3% of all new papers.

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Latest papers 155

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HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models
Jul 5, 2026Angen Ye, Weijie Ke, Xiaofeng Wang +7RL for Robotics Long-Horizon Robotic Manipulation
Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · GigaAI, Beijing, China +1
HEFT: Heavy-Payload Full-size Humanoid Teleoperation with Privileged Motion Guidance and Windowed Payload Curriculum
Jul 2, 2026Chenxin Liu, Qingzhou Lu, Guangxiao Yang +4RL for Robotics Robot Trajectory Tracking
Tsinghua University · RobotEra · Shanghai Qizhi Institute
Learning Expert Strategy for Autonomous Robotic Endovascular Intervention via Decoupled Procedural Execution
Jun 30, 2026Yanxi Chen, Tianliang Yao, Shaolong Tang +5RL for Robotics Hierarchical RL
Department of Control Science and Engineering, College of Electronics and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China · School of Mechanical Engineering, Tongji University, Shanghai 200092, China · Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China +2
Stage-Transition Dense Reward Modeling for Reinforcement Learning
Jun 30, 2026Yang Yang, Bingjie Chen, Zihan Wang +4RL for Robotics Long-Horizon Robotic Manipulation
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China. · Zerith Robotics, Shenzhen 518055, China.
Grasp-Oriented Non-Prehensile Manipulation via Learning a Graspability Field
Jun 29, 2026Licheng Zhong, Gim Hee LeeRL for Robotics Robotic Grasping
Department of Computer Science, National University of Singapore, Singapore
Vision-Language Procedural Reasoning for Context-Aware Reward Modeling of Robotic Endovascular Guidewire Navigation
Jun 29, 2026Wentong Tian, Jiyuan Zhao, Tianliang Yao +4RL for Robotics Robotic Endovascular Intervention
Department of Control Science and Engineering, College of Electronic and Information Engineering, and Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China. · Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong SAR 999077, China. · Shanghai Operation Robot Co., Ltd., Shanghai 201318, China.
Learning to Throw: Agile and Accurate Cable-Suspended Payload Delivery with a Quadrotor
Jun 25, 2026Yifan Zhai, Elia Raimondi, Yunfan Ren +4RL for Robotics Robotics Simulation
Robotics and Perception Group, Department of Informatics, University of Zurich, Switzerland
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Jun 25, 2026Raymond Yu, William Huey, Mustafa Mukadam +2RL for Robotics Robot Policy Adaptation
University of Washington · Amazon FAR
Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
Jun 25, 2026Jonathan Green, Jiaxu Xing, Nico Messikommer +2RL for Robotics Aerial Robotics
Robotics and Perception Group, University of Zurich
Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
Jun 25, 2026Ilia LarchenkoRL for Robotics Bimanual Robotic Manipulation
Independent Researcher
RobOralScan: Learning Active Intraoral Scanning for Robotic Dental Reconstruction
Jun 25, 2026Jinhyung Lee, Haeun Yun, Siwon Kim +4RL for Robotics Robot Policy Learning
DGIST
Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?
Jun 24, 2026Tyler Ga Wei Lum, Kushal Kedia, C. Karen Liu +1RL for Robotics Robot Skill Learning
1Stanford University · 2Cornell University
TaskNPoint: How to Teach Your Humanoid to Hit a Backhand in Minutes
Jun 24, 2026Blake Werner, Ilona Demler, Pietro Perona +1RL for Robotics Robot Skill Learning
California Institute of Technology
FORCE: Efficient VLA Reinforcement Fine-Tuning via Value-Calibrated Warm-up and Self-Distillation
Jun 24, 2026Shuyi Zhang, Yunfan Lou, Hongyang Cheng +8RL for Robotics Vision-Language-Action Models
Institute of Automation, Chinese Academy of Sciences, Beijing, China · Beijing Academy of Artificial Intelligence, Beijing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China
Memory-Efficient Policy Libraries with Low-Rank Adaptation in Reinforcement Learning
Jun 24, 2026Samuel Valland Lyngset, Tor Viljen Raanaas, Gard Sveipe +4RL for Robotics Low-Rank Adaptation
Department of Informatics, University of Oslo
RMTL: Reinforced Micro-task Learning for Long-Horizon Manipulation with VLM Rewards
Jun 24, 2026Anıl Can Ateş, Orhan Kahraman, Cihan TopalRL for Robotics Long-Horizon Robotic Manipulation
Istanbul Technical University
DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
Jun 24, 2026Zhinan Yu, Junyan Xu, Jiazhao Zhang +9RL for Robotics Robotic Grasping
National University of Defense Technology · CFCS, Peking University · Defense Innovation Institute, Academy of Military Sciences +2
TACTFUL: Tactile-Driven Exploration For Object Localization and Identification in Confined Environments
Jun 23, 2026Shivani Kamtikar, Chung Hee Kim, Camilla Tabasso +3RL for Robotics Robotic Tactile Sensing
Amazon Fulfillment Technologies & Robotics, Westborough, MA, USA. · Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA. · Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation
Jun 22, 2026Sikai Li, Shuning Li, Zhenyu Wei +3RL for Robotics Humanoid Loco-Manipulation
University of North Carolina at Chapel Hill · University of California, Berkeley
Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation
Jun 22, 2026Yang Yang, Yuchuang Tong, Zhengtao Zhang +6RL for Robotics Robot Skill Learning
Institute of Automation, Chinese Academy of Sciences, Beijing 100049, China · Intelligent Manufacturing Institute, HFUT, Hefei 230051, China · School of Electrical Engineering and Automation, Anhui University, Hefei 230061, China +1
HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning
Jun 22, 2026Jingyi Liu, Zhaohong Mai, ShunSen He +5RL for Robotics Vision-Language-Action Models
1CloudRobo Lab, Huawei Cloud Computing Technologies Co., Ltd. · 2South China University of Technology.
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
Jun 21, 2026Zechu Li, Yufeng Jin, Puze Liu +2RL for Robotics Dexterous Manipulation
Department of Computer Science, TU Darmstadt, Germany. · Honda Research Institute Europe GmbH, Offenbach, Germany. · DFKI, Research Department SAIROL, Darmstadt, Germany. +2
Pose-Agnostic Robotic Functional Grasping via Observation-Action Canonicalization
Jun 19, 2026Le Qiu, Cole Harrison, Jiankai Sun +5RL for Robotics Robotic Grasping
Tsinghua University · Amazon · Stanford University +1
A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems
Jun 18, 2026Junzhe Xu, Zecui Zeng, Lusong Li +2RL for Robotics Mobile Robotics
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · JD Explore Academy, Beijing, China.
Temporal Self-Imitation Learning
Jun 18, 2026Yinsen Jia, Boyuan ChenRL for Robotics Long-Horizon Robotic Manipulation
Duke University
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
Jun 17, 2026Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik +2RL for Robotics Reinforcement Learning
Department of Computing Science, University of Alberta · National Research Council Canada · School of Electrical Engineering and Computer Science, University of Ottawa +3
ROVE: Unlocking Human Interventions for Humanoid Manipulation via Reinforcement Learning
Jun 15, 2026Wei Xiao, Weiliang Tang, Yuying Ge +4RL for Robotics Robotic Manipulation
1XPENG Robotics · 2Fudan University · 3The Chinese University of Hong Kong +1
Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty
Jun 15, 2026Shivansh Pratap Singh, Samaksh Ujjwal, Ishita Chaudhary +3Robot Dynamics Identification RL for Robotics
International Institute of Information Technology Hyderabad, India. · Department of Computer Science, University of Manchester, UK.
LoComposition: Terrain-Adaptive Energy-Efficient Quadruped Locomotion without Gait Priors
Jun 14, 2026Loukas Kordos, Leonard T. Franz, Simon Rappenecker +4Legged Robotics RL for Robotics
Technical University of Munich. · University of Tübingen. · Hertie Institute for Clinical Brain Research & Center for Integrative Neuroscience. +1
Elastic Queries Reinforcement Learning: Self-Aware Policy Execution for VLA Models
Jun 12, 2026Ge Wang, Xinyu Tan, Xiang Li +11RL for Robotics Efficient VLM Inference
Ising AI · CUHK-Shenzhen · PKU

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

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

Angen Ye, Weijie Ke, Xiaofeng Wang, Xinze Chen+6

Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · GigaAI, Beijing, China+1

RL for Robotics Long-Horizon Robotic Manipulation Robotic RL World Action Models

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World-action (WA) models can generate long-horizon action chunks for general-purpose robotic manipulation, but they remain vulnerable to calibration, perception, and contact-dynamics errors in real-world precision tasks, often failing in the final few millimeters of alignment or insertion. We propose HALO-WA, a hybrid-attention latent-guided online reinforcement learning (RL) framework for WA models, which leverages latent features and action priors from the WA generation process through a lightweight actor-critic adapter to enable fast online adaptation to real deployment errors. HALO-WA introduces a hybrid-attention structure that preserves the temporal consistency of action chunks while reading task-relevant information from WA latents conditioned on visual context and end-stage correction requirements, thereby producing refined action chunks. We validate HALO-WA on four real-world precision manipulation tasks, where it improves the average success rate from 26.4% for WA-base to 87.1%, outperforming the strongest baseline by 19.2 percentage points while requiring only 45--75 minutes of online training per task. To facilitate reproducibility, we further conduct supplementary simulation experiments in RoboTwin and release the code at https://github.com/YeanRoot/HALO-WA.

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