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.

OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation
May 15, 2026Yunyang Mo, Jian Li, Qiwei Wu +2Robotic RL Human-in-the-Loop AI
The Hong Kong University of Science and Technology (Guangzhou)
NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
May 15, 2026Zhefan Xu, Hanyu Jin, Kenji ShimadaRobot Policy Learning Robot Navigation
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
What to Ignore, What to React: Visually Robust RL Fine-Tuning of VLA Models
May 13, 2026Yuanfang Peng, Jingjing Fu, Chuheng Zhang +6VLM Robustness Domain Generalization
1Hong Kong University of Science and Technology · 2Microsoft Research Asia · 3Zhejiang University
Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization
May 12, 2026Alejandro Murillo-Gonzalez, Mahmoud Ali, Lantao LiuMulti-Objective Reinforcement Learning Policy Optimization
Indiana University–Bloomington
3D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
May 12, 2026Chiara Castellani, Enrico Turco, Domenico PrattichizzoRobotic Control Robot Navigation
Humanoids and Human Centered Mechatronics Istituto Italiano di Tecnologia 16163, Genoa, Italy · Dept. of Information Engineering and Mathematics University of Siena 53100, Siena, Italy
TMRL: Diffusion Timestep-Modulated Pretraining Enables Exploration for Efficient Policy Finetuning
May 12, 2026Matthew M. Hong, Jesse Zhang, Anusha Nagabandi +1Robot Policy Adaptation Robot Skill Learning
University of Washington · Amazon FAR
EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
May 12, 2026Zhikai Zhao, Chuanbo Hua, Federico Berto +4Evolutionary Optimization Robot Navigation
1KAIST · 2Radical Numerics · 3UC Riverside +1
RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking
May 11, 2026Andrew Choi, Wei XuQ-Learning Robotic RL
Horizon Robotics
XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies
May 11, 2026Daniel Palenicek, Florian Vogt, Joe Watson +3Robotic RL Learning from Demonstration
Technical University of Darmstadt · KTH Royal Institute of Technology · University of Oxford +2
Active Embodiment Identification with Reinforcement Learning for Legged Robots
May 8, 2026Nico Bohlinger, Jan PetersLegged Robotics Robotic RL
Department of Computer Science, Technical University of Darmstadt, Germany. · Robotics Institute Germany (RIG); German Research Center for AI (DFKI); hessian.AI.
When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
May 6, 2026Lakshita Dodeja, Ondrej Biza, Shivam Vats +5Q-Learning Robotic RL
1Robotics and AI Institute · 2Brown University · 3Northeastern University
LineRides: Line-Guided Reinforcement Learning for Bicycle Robot Stunts
May 6, 2026Seungeun Rho, Shamel Fahmi, Jeonghwan Kim +3Reinforcement Learning Robot Skill Learning
1RAI Institute, Cambridge, MA, USA · Georgia Institute of Technology, Atlanta, GA, USA
SOAR: Real-Time Joint Optimization of Order Allocation and Robot Scheduling in Robotic Mobile Fulfillment Systems
May 5, 2026Yibang Tang, Yifan Yang, Jingyuan Wang +2Mobile Robotics Robotic RL
School of Computer Science and Engineering, Beihang University, Beijing, China · Geekplus Technology Co., Ltd., Beijing, China
DexSim2Real: Foundation Model-Guided Sim-to-Real Transfer for Generalizable Dexterous Manipulation
May 3, 2026Zijian Zeng, Fei Ding, Huiming Yang +2Domain Randomization Dexterous Manipulation
Tsinghua University · Alibaba Group · Bengbu University +1
Towards Efficient and Expressive Offline RL via Flow-Anchored Noise-conditioned Q-Learning
May 3, 2026Sungyoung Lee, Dohyeong Kim, Eshan Balachandar +2Distributional RL Q-Learning
The University of Texas at Austin, Austin, TX, USA · Independent Researcher, Seoul, South Korea.
Learning to Race in Minutes: Infoprop Dyna on the Mini Wheelbot
May 1, 2026Devdutt Subhasish, Henrik Hose, Sebastian TrimpeRobot Skill Learning Autonomous Racing
Institute for Data Science in Mechanical Engineering, RWTH Aachen University, 52062 Aachen
E $^2$ DT: Efficient and Effective Decision Transformer with Experience-Aware Sampling for Robotic Manipulation
Apr 30, 2026Kaiyan Zhao, Borong Zhang, Yiming Wang +4Long-Horizon Robotic Manipulation Transformer
School of Computer Science, Wuhan University, Wuhan 430072, China · State Key Lab of Internet of Things for Smart City and Department of Computer Information Science, University of Macau, Macao 999078, China
RHyVE: Competence-Aware Verification and Phase-Aware Deployment for LLM-Generated Reward Hypotheses
Apr 30, 2026Feiyu Wu, Xu Zheng, Zhuocheng Wang +2Reinforcement Learning LLM-Guided RL
School of Cyber Engineering, Xidian University
Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
Apr 30, 2026Buqing Ou, Frederike DümbgenTabular Foundation Models Robot Policy Learning
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Inria, Département d’informatique de l’ENS, CNRS, PSL Research University, Paris, France
TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning
Apr 28, 2026Dominik Żurek, Kamil Faber, Marcin Pietron +2Continual Learning Robotic RL
AGH University of Krakow, Poland · American University, Washington, DC, USA
Distributional Reinforcement Learning via the Cramér Distance
Apr 26, 2026Vanya Aziz, Ivo Nowak, E. M. T HendrixDistributional RL Robotic RL
HAW Hamburg · de Málaga
Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization
Apr 22, 2026Kevin Godin-Dubois, Anil Yaman, Anna V. KononovaRobotic Control Robotic RL
Vrije Universiteit Amsterdam, Netherlands · LIACS, Leiden Universiteit, Netherlands
Learning Hybrid-Control Policies for High-Precision In-Contact Manipulation Under Uncertainty
Apr 21, 2026Hunter L. Brown, Geoffrey Hollinger, Stefan LeeRobotic Control Contact-Rich Robotic Manipulation
Collaborative Robotics and Intelligent Systems Institute, Oregon State University, Corvallis, OR
Does "Do Differentiable Simulators Give Better Policy Gradients?" Give Better Policy Gradients?
Apr 20, 2026Ku Onoda, Paavo Parmas, Manato Yaguchi +1Policy Gradient Robotic RL
The University of Tokyo, Tokyo, Japan
Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)
Apr 17, 2026Hürkan Şahin, Van Huyen Dang, Erdi Sayar +2Reward Shaping Autonomous Drone Racing
Automatic Control Group (RAT), Paderborn University, 33098 Paderborn, Germany
Abstract Sim2Real through Approximate Information States
Apr 16, 2026Yunfu Deng, Yuhao Li, Josiah P. HannaSim-to-Real Transfer State Abstraction
Department of Computer Sciences, University of Wisconsin–Madison, Madison, WI 53706 USA · Manning College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA 01003 USA
Chain of Uncertain Rewards with Large Language Models for Reinforcement Learning
Apr 15, 2026Shentong MoReward Design for RL LLM-Guided RL
Carnegie Mellon University
E2E-Fly: An Integrated Training-to-Deployment System for End-to-End Quadrotor Autonomy
Apr 14, 2026Fangyu Sun, Fanxing Li, Linzuo Zhang +5Quadrotor Control Sim-to-Real Transfer
School of Automation and Perception, Shanghai Jiao Tong University, China
From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
Apr 14, 2026Lidor Erez, Shahaf S. Shperberg, Ayal TaitlerRobot Task Planning Constrained Motion Planning
Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be’er Sheva, Israel · Stein Faculty of Computer and Information Science, Ben-Gurion University of the Negev, Be’er Sheva, Israel
AIM: Intent-Aware Unified world action Modeling with Spatial Value Maps
Apr 13, 2026Liaoyuan Fan, Zetian Xu, Chen Cao +3World Action Models Robotic Manipulation
INFIFORCE Intelligent Technology Co., Ltd., Hangzhou, China · The University of Hong Kong, Hong Kong SAR, China · Shanghai Jiao Tong University, Shanghai, China

May 15, 2026J/K move · Enter open · S save

OHP-RL: Online Human Preference as Guidance in Reinforcement Learning for Robot Manipulation

Yunyang Mo, Jian Li, Qiwei Wu, Yihang Kang+1

The Hong Kong University of Science and Technology (Guangzhou)

Robotic RL Human-in-the-Loop AI Robotic Manipulation Preference-Based RL

Open paper AskPDF arXiv

While reinforcement learning (RL) enables robots to acquire skills autonomously, its real-world deployment is severely limited by inefficient and unsafe exploration. Human-in-the-loop interventions offer a practical solution, yet existing methods typically exploit these interventions as auxiliary training signals, without fully capturing the richer information they provide about when and how autonomy should be guided. Human interventions often encode relative preferences over behavior under safety and task constraints, rather than prescribing exact actions to imitate. Motivated by this perspective, we propose Online Human Preference as Guidance in Reinforcement Learning (OHP-RL), a framework that leverages human interventions as preference information to guide policy learning. OHP-RL introduces a state-dependent preference gate that adaptively regulates when and to what extent human interventions should shape policy learning. This design enables the agent to benefit from intermittent and imperfect human feedback while preserving autonomous exploration and stable policy optimization. We evaluate OHP-RL on three challenging real-world contact-rich manipulation tasks on a Franka robot. Across all tasks, OHP-RL consistently achieves strong success rates, faster convergence, and substantially lower human intervention effort than prior approaches. Moreover, the learned policies exhibit more stable and human-aligned behavior throughout training.