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.

Gym-Anything for Robotics: Scaling physical simulations with digital agents
Oct 29, 2026Pranjal Aggarwal, Sean WelleckRobotics Simulation Robotic RL
Carnegie Mellon University
Humanoid Dodgeball: Learning Fast Dynamic Obstacle Avoidance Motions for Humanoids
Oct 29, 2026Aoqian Zhang, Chunzheng Wang, Haoran Chen +4Dynamic Obstacle Avoidance Humanoid Robot Control
Department of Electrical and Computer Engineering, National University of Singapore, Singapore. · Shenzhen EngineAI Robotics Technology Co., Ltd., China. · School of Information and Communication Engineering, Dalian University of Technology, China.
Event2Reward: Learning Robotic Rewards from Progress Relations
Oct 29, 2026Junnan Nie, Zitian Fan, Jiachen Zhang +14Robotic RL Reward Design for RL
Peking University · JD Explore Academy
Robotic Data Inoculation
Oct 28, 2026Qiyang Li, Zhiyuan Zhou, Charles Xu +1Robotic Data Collection Data-Efficient Robot Learning
UC Berkeley
CSO: Refining Robotic Policies via Skill Distribution Alignment and Skill-Grained Optimization
Oct 28, 2026Zhiyuan Xiang, Xiang Deng, Wanjie Tao +3Robot Policy Learning Robot Skill Learning
Harbin Institute of Technology (Shenzhen) · Mashang Consumer Finance Co., Ltd. · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China
Learning from Relative Action Credit for Escaping Local Minima
Oct 27, 2026Giovanni Minelli, Giulio Turrisi, Lorenzo Amatucci +2Reinforcement Learning Robotic RL
Istituto Italiano di Tecnologia, Genova, Italy
Echo: Scaling Visual Reinforcement Learning with Off-policy Experience
Oct 27, 2026Gagan KhandateVisual RL Robotic RL
Rendering-Free Event Frame Simulation for Fast Reinforcement Learning of Autonomous Robot Navigation
Oct 27, 2026Dequan Ou, Austin Lopez, Quentin Missinne +2Robotics Simulation Event-Based Vision
All authors are with the Micro Air Vehicle Lab of the Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
Rollback: Experience Backtracking in World Models for Policy Improvement
Oct 26, 2026Jiayi Luo, Yang Zhang, Liliang Chen +13Robot Policy Learning Robotic RL
AgiBot · BUAA · NUS
Enhancing Deep Reinforcement Learning for Robotic Motion Planning via Task Decomposition
Oct 26, 2026Yuanzhi He, Victor Romero-Cano, Jiayu Xu +2Reinforcement Learning Robotic RL
School of Computational and Mathematical Sciences Cardiff University, United Kingdom
RBGA: Residual-Budget Guide Arbitration for Offline-to-Online Safe Reinforcement Learning in Dexterous Manipulation
Oct 24, 2026Zongyou Yu, Ao Zhou, Xiongbo Shi +7Reinforcement Learning Safe RL
Tongji University · Shanghai Innovation Institute · Xiaomi EV +2
RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Oct 23, 2026Xi Chen, Fengqi Hu, Zeyu Shen +5Robust RL RL Benchmarks
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. · Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. · University of California San Diego, La Jolla, CA 92093, USA. +3
HoloBoxing: A Simple Recipe for Humanoid Boxing with 7 Minutes of Single-GPU Training
Oct 23, 2026Xihan Ma, Bo Zhang, Boxiong Li +3Humanoid Robotics Humanoid Robot Control
$R^2E:$ A Role-driven Reward Evolutionary Framework for Automated Reward Function Design
Oct 23, 2026Shouhao Chang, Xuan Liu, Hongye Zhu +2Reward Design for RL LLM-Guided RL
Hunan University · Central South University
PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots
Oct 23, 2026Nasim Fallahiarezoodar, Zheng H. ZhuSpace Robotics Bimanual Robotic Manipulation
Department of Earth and Space Science and Engineering, York University, M3J1P3 · Mechanical Engineering, York University, M3J1P3
SEVI: Safe Evidential Real-World Reinforcement Learning
Oct 22, 2026Shaekh Mohammad Shithil, Lantao LiuSafe RL Robotic RL
Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN 47408 USA
MPBRS: Multi-Potential-Based Reward Shaping for Unified Reward Configuration in Humanoid Locomotion
Oct 21, 2026Van Chung Nguyen, Hung Manh LaReward Shaping Reward Design for RL
are with the Advanced Robotics and Automation (ARA) Lab, Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA.
State-Dependent Objective Coordination for End-to-End Balance and Navigation
Oct 21, 2026Xuhao Huang, Xitao Xu, Yuxin Zheng +2Robotic Control Robot Navigation
GGraph: A GPU-Accelerated Library for Graph-Based Robot Deployment and Learning
Oct 20, 2026Yash Rampuria, Rohan Garg, Daniele Gammelli +1Robot Learning Robotic RL
Stanford University, CA
Physical Canvas: A Vision Tactile World Model For Robot Policy Evaluation And Learning
Oct 20, 2026Zebin Xing, Yuhang Zheng, Yupeng Zheng +8World Models for Robotics Tactile World Models
Institute of Automation, Chinese Academy of Sciences · Anyverse Robotics · Open-Phoenix Team +3
VaDar: Implicit Goal Conditioning via Value Disaggregation
Oct 19, 2026Shashwat Saxena, Mehul Goel, Sreyas Venkataraman +2Long-Horizon RL Reinforcement Learning
Carnegie Mellon University
Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control
Oct 16, 2026Lorenzo Magnino, Antonio Marino, Michael Amir +2Real-Time Robot Control Robotic Control
University of Cambridge — Cambridge, United Kingdom. · College of Engineering and Computing at George Mason University
Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors
Oct 16, 2026Carl Qi, Weijun Dong, Xiaojie Wang +8Robot Policy Learning Robotic RL
Amazon · UT Austin · UC San Diego +2
Value as Memory: Online Value-Guided Post-training for General Robot Policies
Oct 15, 2026Wenye Yu, Yijin Chen, Kaiyi Xiong +7Robotic RL RL Post-Training
Shanghai Jiao Tong University · Shanghai Innovation Institute · Noematrix Ltd.
Progress Reward Model for Reinforcement Learning via Large Language Models
Oct 15, 2026Xiuhui Zhang, Ning Gao, Xingyu Jiang +4Reward Design for RL LLM-Guided RL
Beihang University, 37 Xueyuan Road, Haidian District, Beijing
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Oct 14, 2026Zhefei Gong, Hefeifei Jiang, Zehao Wang +1Mobile Manipulation Robotic RL
Northeastern University · University of California, Riverside · Georgia Institute of Technology
Hyperspherical Contrastive Representations Unlock Planning and Exploration
Oct 14, 2026Jonghae Park, Sungil Son, Jonghun Shin +3Robotic RL Robot Motion Planning
Seoul National University · Princeton University
HiFun: a Hierarchical Framework for Efficient Functional Dexterous Manipulation Learning
Oct 14, 2026Linyi Huang, Guowei Huai, Weibin Liu +5Robotic Manipulation Robot Skill Learning
HKUST (Guangzhou), China · HKUST, Hong Kong (China) · ETH Zurich, Switzerland
WoVR+: World Model as Reliable Simulator for Versatile VLA Policy Optimization with RL
Oct 13, 2026Shangqing Zhou, Zhennan Jiang, Ning Ma +9Robotic RL RL for VLMs
Institute of Automation, Chinese Academy of Sciences. · School of Artificial Intelligence, University of Chinese Academy of Sciences. · Zhongguancun Academy. +3
Agentic Robot Creation through Generation, Verification, and Revision
Oct 13, 2026Xuebing Li, Yibo He, Sitong Liu +2Agentic AI for Robotics Robotics
ShanghaiTech University

Oct 29, 2026J/K move · Enter open · S save

Gym-Anything for Robotics: Scaling physical simulations with digital agents

Pranjal Aggarwal, Sean Welleck

Carnegie Mellon University

Robotics Simulation Robotic RL Embodied Agent Benchmarks Code Generation for Robotics

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Realistic, fast robotics simulations could enable large-scale training of embodied AI models that transfer to the real world. However, despite decades of progress, available simulations cover only a narrow range of real-world tasks and physical phenomena. The core bottleneck is that creating better simulations requires significant expert effort and therefore does not scale. To address this, we introduce Gym-Anything for Robotics, a framework in which coding agents create realistic robotics simulations. Our core insight is that simulation creation is a coding agent task, so improvements in coding agents translate directly into richer and more faithful simulations. We select simulations based on U.S. GDP data to cover economically valuable physical work, define what makes a good simulation and a good task, and develop an adversarial creation-audit loop that checks each simulation and task against these definitions. Using this pipeline, we build RoboWorld, a set of 25 simulations with 25 test and 175 training RL environments of varying difficulty. Even the best frontier models fail on every RoboWorld test task, achieving an average reward of only 0.06. Building on RoboWorld, we introduce several new ways for frontier models to improve, including in-context learning across repeated attempts, a privileged teacher that reads the simulator state and teaches a student in context, self-assisted learning from the model's own privileged experience, and code policies that generate demonstrations across varied scenes to train vision-language-action models (VLAs) such as

π_{0.5}

. Each of these substantially improves performance. We release our pipeline, RL environments, and benchmark to facilitate future research. Code and data are available at https://gym-anything.github.io/robotics