Robotic RL

RL: Reinforcement Learning

Robotic reinforcement learning studies how robots can learn actions from rewards and experience in tasks such as locomotion, navigation, and contact-rich manipulation. Common approaches include model-free methods such as PPO and SAC, hierarchical and model-based control, and reinforcement-learning fine-tuning of policies pretrained from demonstrations or large datasets. Simulation and parallel training make it possible to explore many behaviors, while domain randomization, system identification, and real-robot fine-tuning help transfer policies to hardware. A central challenge is achieving sample-efficient learning without sacrificing safety, robustness, or performance when sensors, dynamics, and task conditions differ from training.

Latest papers

307 papers49 papers in the last four weeks, up 14% on the four weeks before. 0.3% of all new papers.

RL-Based Coverage Path Planning for Deformable Objects on 3D Surfaces
Mar 3, 2026Yuhang Zhang, Jinming Ma, Feng WuCoverage Path Planning Deformable Object Manipulation
School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China · Xiaomi Robotics Lab, Beijing, China
Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards
Mar 2, 2026Faisal Mohamed, Catherine Ji, Benjamin Eysenbach +1RL Exploration Robotic RL
Mila-Quebec AI Institute · Université de Montréal · Princeton University
Hybrid TD3: Overestimation Bias Analysis and Stable Policy Optimization for Hybrid Action Space
Mar 1, 2026Thanh-Tuan Tran, Thanh Nguyen Canh, Nak Young Chong +1Hybrid Action Space RL Reinforcement Learning
University of Engineering and Technology, Vietnam National University, 10000, Hanoi, Vietnam. · School of Information Science, Japan Advanced Institute of Science and Technology, Nomi, 923-1211, Ishikawa, Japan. · Department of Robotics, Hanyang University, Ansan, 15588, Gyeonggi, Korea.
HierKick: Hierarchical Reinforcement Learning for Vision-Guided Soccer Robot Control
Mar 1, 2026Yizhi Chen, Zheng Zhang, Zhanxiang Cao +7Robotic Control Robotic RL
Tongji University, Shanghai, China. · Shanghai Innovation Institute, Shanghai, China. · Shanghai Jiao Tong University, Shanghai, China. +2
Keyframe-Guided Structured Rewards for Reinforcement Learning in Long-Horizon Laboratory Robotics
Feb 28, 2026Yibo Qiu, Shu'ang Sun, Haoliang Ye +2Long-Horizon Robotic Manipulation Robotic RL
Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, China · School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
Learning Robust Control Policies for Inverted Pose on Miniature Blimp Robots
Feb 27, 2026Yuanlin Yang, Lin Hong, Fumin ZhangRobotic Control Aerial Robotics
Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Learning to Build: Autonomous Robotic Assembly of Stable Structures Without Predefined Plans
Feb 27, 2026Jingwen Wang, Johannes Kirschner, Paul Rolland +2Robotic Construction Sequential Decision Making
Lab of Creative Computation, ´Ecole Polytechnique F´ed´erale de Lausanne · Swiss Data Science Center
Actor-Critic Pretraining for Proximal Policy Optimization
Feb 27, 2026Andreas Kernbach, Amr Elsheikh, Nicolas Grupp +2Robotic RL Sample-Efficient RL
Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · Institute of Industrial Manufacturing and Management IFF, University of Stuttgart, Germany · Institute for Control Engineering of Machine Tools and Manufacturing Units ISW, University of Stuttgart, Germany +1
Mollified Value Learning
Feb 26, 2026Hrishikesh Viswanath, Juanwu Lu, S. Talha Bukhari +4Offline RL Robotic RL
Department of Computer Science, Purdue University, USA · College of Engineering, Purdue University, USA · DEVCOM Army Research Laboratory, USA
SPARR: Simulation-based Policies with Asymmetric Real-world Residuals for Assembly
Feb 26, 2026Yijie Guo, Iretiayo Akinola, Lars Johannsmeier +3Robotic Assembly Sim-to-Real Transfer
Nvidia · University of Washington
DexRepNet++: Learning Dexterous Robotic Manipulation with Geometric and Spatial Hand-Object Representations
Feb 25, 2026Qingtao Liu, Zhengnan Sun, Yu Cui +5Dexterous Manipulation Representation Learning
the College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · the College of Control Science and Engineering and the State Key Laboratory of Industrial Control Technology, Zhejiang University, China · the Department of Computer Science, National University of Singapore, 15 Computing Dr, 117418, Singapore
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination
Feb 25, 2026Chenyv Liu, Wentao Tan, Lei Zhu +4Vision-Language-Action Models Robotic RL
Tongji University · University of Technology Sydney · University of Electronic Science and Technology of China +1
CACTO-BIC: Scalable Actor-Critic Learning via Biased Sampling and GPU-Accelerated Trajectory Optimization
Feb 23, 2026Elisa Alboni, Pietro Noah Crestaz, Elias Fontanari +1Actor-Critic Methods Trajectory Optimization
Dept. of Industrial Engineering, University of Trento, Italy · LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
What Matters for Simulation to Online Reinforcement Learning on Real Robots
Feb 23, 2026Yarden As, Dhruva Tirumala, René Zurbrügg +4RL for Robotics Robotic RL
ETH Zurich · Google DeepMind
Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systems
Feb 18, 2026Jialiang Fan, Shixiong Jiang, Mengyu Liu +1Adversarial Attacks Black-Box Adversarial Attacks
University of Notre Dame, Notre Dame, IN, USA. · Washington State University Tri-Cities, Richland, WA, USA.
Efficient Knowledge Transfer for Jump-Starting Control Policy Learning of Multirotors through Physics-Aware Neural Architectures
Feb 17, 2026Welf Rehberg, Mihir Kulkarni, Philipp Weiss +1Robotic RL Cross-Embodiment Robot Learning
Department of Engineering Cybernetics at the Norwegian University of Science and Technology, O.S. Bragstads Plass 2D, 7034, Trondheim, Norway
ManeuverNet: A Soft Actor-Critic Framework for Precise Maneuvering of Double-Ackermann-Steering Robots with Optimized Reward Functions
Feb 16, 2026Kohio Deflesselle, Mélodie Daniel, Aly Magassouba +2Robotic RL Agricultural Robotics
Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France. · School of Computer Science, University of Nottingham, UK. · Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.
Simulation-based Learning of Electrical Cabinet Assembly Using Robot Skills
Feb 16, 2026Arik Laemmle, Balázs András Bálint, Philipp Tenbrock +4Robotic Assembly Robot Skill Learning
formerly Fraunhofer Institute for Manufacturing Engineering and Automation IPA, Stuttgart, Germany · formerly Institut für Strahlwerkzeuge IFSW, University of Stuttgart, Stuttgart, Germany · Research Laboratory on Engineering and Management Intelligence, Institute for Computer Science and Control SZTAKI, Budapest, Hungary +1
TWISTED-RL: Hierarchical Skilled Agents for Knot-Tying without Human Demonstrations
Feb 16, 2026Guy Freund, Tom Jurgenson, Matan Sudry +1Robotic Manipulation Robot Skill Learning
Reichman University · Technion – Israel Institute of Technology · The University of Texas at Austin
Learning Human-Like Badminton Skills for Humanoid Robots
Feb 9, 2026Yeke Chen, Shihao Dong, Xiaoyu Ji +12Humanoid Robot Control Robotic RL
Adaptive Robotic Controls Lab (ArcLab), Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China · EngineAI Robotics Technology Co., Ltd., Shenzhen, China
Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions
Feb 7, 2026Yicheng Yang, Ruijiao Li, Lifeng Wang +7Reinforcement Learning Robotic RL
Emergence of Physical Intelligence via Controllable Information Production
Jan 30, 2026Tristan Shah, Stas TiomkinRobotic RL Reinforcement Learning
Department of Computer Science, Texas Tech University
UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations
Dec 12, 2025Tingyu Yuan, Biaoliang Guan, Wen Ye +8Cross-Embodiment Robot Learning Robotic RL
CASIA · UCAS · XJTU +3
A Primer on SO(3) Action Representations in Deep Reinforcement Learning
Oct 13, 2025Martin Schuck, Sherif Samy, Angela P. SchoelligRobotic RL RL Exploration
Technical University of Munich · University of Girona
What Matters in RL-Based Methods for Object-Goal Navigation? An Empirical Study and A Unified Framework
Oct 2, 2025Hongze Wang, Boyang Sun, Jiaxu Xing +5Robotic RL Robotic Perception
Computer Vision and Geometry Group, ETH Zurich, Switzerland · Robotics and Perception Group, University of Zurich, Switzerland · Robotic Systems Lab, ETH Zurich, Switzerland +2
HARL-A: An Extensible Benchmark Framework for Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab
Sep 26, 2025Isaac Peterson, Christopher Allred, Jacob Morrey +1RL Benchmarks Adversarial MARL
Utah State University · US DEVCOM Army Research Laboratory
Robot Drummer: Learning Rhythmic Skills for Humanoid Drumming
Jul 15, 2025Asad Ali Shahid, Francesco Braghin, Loris RovedaLong-Horizon Robotic Manipulation Robot Skill Learning
IDSIA, SUPSI, Lugano, Switzerland · Department of Mechanical Engineering, Politecnico di Milano, Italy
An Introduction to Zero-Order Optimization Techniques for Robotics
Jun 27, 2025Armand Jordana, Jianghan Zhang, Joseph Amigo +1Zeroth-Order Optimization Robotic Optimization
Machines in Motion Laboratory, New York University, USA
Adaptive Reinforcement Learning for Unobservable Random Delays
Jun 17, 2025John Wikman, Alexandre Proutiere, David BromanReinforcement Learning Robotic RL
EECS and Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden.
Shared Modular Recurrence in Contextual MDPs for Universal Morphology Control
Jun 10, 2025Laurens Engwegen, Max Weltevrede, Caroline Horsch +2Robotic Control Reinforcement Learning
Delft University of Technology Delft, The Netherlands

Mar 3, 2026J/K move · Enter open · S save

RL-Based Coverage Path Planning for Deformable Objects on 3D Surfaces

Yuhang Zhang, Jinming Ma, Feng Wu

School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China · Xiaomi Robotics Lab, Beijing, China

Coverage Path Planning Deformable Object Manipulation Contact-Rich Robotic Manipulation Robotic RL Robotic Tool Use

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Currently, manipulation tasks for deformable objects often focus on activities like folding clothes, handling ropes, and manipulating bags. However, research on contact-rich tasks involving deformable objects remains relatively underdeveloped. When humans use cloth or sponges to wipe surfaces, they rely on both vision and tactile feedback. Yet, current algorithms still face challenges with issues like occlusion, while research on tactile perception for manipulation is still evolving. Tasks such as covering surfaces with deformable objects demand not only perception but also precise robotic manipulation. To address this, we propose a method that leverages efficient and accessible simulators for task execution. Specifically, we train a reinforcement learning agent in a simulator to manipulate deformable objects for surface wiping tasks. We simplify the state representation of object surfaces using harmonic UV mapping, process contact feedback from the simulator on 2D feature maps, and use scaled grouped convolutions (SGCNN) to extract features efficiently. The agent then outputs actions in a reduced-dimensional action space to generate coverage paths. Experiments demonstrate that our method outperforms previous approaches in key metrics, including total path length and coverage area. We deploy these paths on a Kinova Gen3 manipulator to perform wiping experiments on the back of a torso model, validating the feasibility of our approach.