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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Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies
May 12, 2026Alberta Longhini, David Emukpere, Jean-Michel Renders +1RL for Robotics Reinforcement Learning
Department of Computer Science, Stanford University, CA, USA
UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
May 11, 2026Junjie Lu, Xinyao Qin, Yuhua Jiang +6RL for Robotics Robot Policy Adaptation
University of Technology Sydney · Work conducted at Microsoft Research. · Tsinghua University +1
Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation
May 11, 2026Zhixuan Shen, Jiawei Du, Ziyu Guo +5RL for Robotics Embodied Navigation
School of Computing and Artificial Intelligence, Southwest Jiaotong University, China · Centre for Frontier AI Research, A*STAR, Singapore · School of Computer Science, University of Leeds, UK
Minimizing Worst-Case Weighted Latency for Multi-Robot Persistent Monitoring: Theory and RL-Based Solutions
May 10, 2026Weizhen Wang, Ziheng Wang, Jianping He +2RL for Robotics Markov Decision Processes
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances
May 6, 2026Xinpan Meng, Siyao Huang, JingPu Yang +6RL for Robotics Contact-Rich Robotic Manipulation
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
OGPO: Sample Efficient Full-Finetuning of Generative Control Policies
May 4, 2026Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal +14RL for Robotics RL for Generative Models
Carnegie Mellon University · University of California, Berkeley · University of Washington +1
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
May 4, 2026Lingxiao Kong, Cong Yang, Oya Deniz Beyan +1RL for Robotics Reinforcement Learning
Fraunhofer Institute for Applied Information Technology FIT, Germany · Soochow University, China · University of Cologne, Germany +1
Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
May 4, 2026Shanze Wang, Xinming Zhang, Siwei Cheng +4RL for Robotics Robot Navigation
College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +2
LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning
Apr 30, 2026Hao Chen, Jiaming Liu, Zhonghao Yan +10RL for Robotics Robot Foundation Models
1The Chinese University of Hong Kong · 2State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · 3Simplexity Robotics
Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies
Apr 29, 2026Pokuang Zhou, Yuhao Zhou, Quan Khanh Luu +7Legged Robotics RL for Robotics
Purdue University, West Lafayette, IN, USA. · Istituto Italiano di Tecnologia, Genoa, Italy. · Columbia University, New York, NY, USA.
Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance
Apr 28, 2026Carson Kohlbrenner, Niraj Pudasaini, William Xie +3RL for Robotics Robotic Tactile Sensing
University of Colorado Boulder, 1111 Engineering Drive, Boulder, CO USA.
RL Token: Bootstrapping Online RL with Vision-Language-Action Models
Apr 24, 2026Charles Xu, Jost Tobias Springenberg, Michael Equi +4RL for Robotics Robot Skill Learning
Physical Intelligence
Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systems
Apr 22, 2026Jiabao Ji, Yongchao Chen, Yang Zhang +4RL for Robotics Multi-Robot Motion Planning
UC Santa Barbara · Massachusetts Institute of Technology · Harvard University +2
Visual-Tactile Peg-in-Hole Assembly Learning from Peg-out-of-Hole Disassembly
Apr 22, 2026Yongqiang Zhao, Xuyang Zhang, Zhuo Chen +3RL for Robotics Contact-Rich Robotic Manipulation
Department of Engineering, King’s College London, Strand, London, WC2R 2LS, United Kingdom · Department of Informatics, King’s College London, Strand, London, WC2R 2LS, United Kingdom
FastGrasp: Learning-based Whole-Body Control Method for Fast Dexterous Grasping with Mobile Manipulators
Apr 14, 2026Heng Tao, Yiming Zhong, Zemin Yang +1RL for Robotics Robotic Grasping
ShanghaiTech University
Learning Dexterous Grasping from Sparse Taxonomy Guidance
Apr 5, 2026Juhan Park, Taerim Yoon, Seungmin Kim +10RL for Robotics Robotic Grasping
Department of Artificial Intelligence, Korea University, Seoul, Korea · Korea University of Technology and Education, Cheonan, Korea · Naver AI Lab, Korea +4
BAT: Balancing Agility and Stability via Online Policy Switching for Long-Horizon Whole-Body Humanoid Control
Apr 1, 2026Donghoon Baek, Sang-Hun Kim, Sehoon HaRL for Robotics Humanoid Robot Control
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Samsung Research
CycleRL: Sim-to-Real Deep Reinforcement Learning for Robust Autonomous Bicycle Control
Mar 16, 2026Gelu Liu, Teng Wang, Zhijie Wu +3Robotic Control RL for Robotics
School of Electronics and Communication Engineering and Shenzhen Key Laboratory of Navigation and Communication Integration, Sun Yat-sen University, Shenzhen 518107, China
From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning
Mar 10, 2026Zhanyi Sun, Shuran SongRL for Robotics Robot Policy Learning
1Stanford University.
Latent Policy Steering through One-Step Flow Policies
Mar 5, 2026Hokyun Im, Andrey Kolobov, Jianlong Fu +1RL for Robotics Latent Action Learning
Department of Artificial Intelligence, Yonsei University · Microsoft Research
AdaptManip: Learning Adaptive Whole-Body Object Lifting and Delivery with Online Recurrent State Estimation
Feb 16, 2026Morgan Byrd, Donghoon Baek, Kartik Garg +5RL for Robotics Robotic Manipulation
Georgia Institute of Technology, Atlanta, GA, 30308, USA · Georgia Tech Research Institute, Atlanta, GA, 30308, USA
High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning
Feb 9, 2026Jiarui Zhang, Chengyong Lei, Chengjiang Dai +4RL for Robotics UAV Navigation
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Differential Robotics, Hangzhou 311121, China.
PlatoLTL: Scaling LTL-Guided Multi-Task RL
Jan 30, 2026Jacques Cloete, Mathias Jackermeier, Ioannis Havoutis +1RL for Robotics Constrained RL
Oxford Robotics Institute University of Oxford Oxford, United Kingdom · Department of Computer Science University of Oxford Oxford, United Kingdom
OGPO: One-Step Generative Policy Optimization for Real-Time Robot Control
Jan 28, 2026Guowei Zou, Haitao Wang, Hejun Wu +3Robotic Control RL for Robotics
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China · Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China
Reversible Simplex Supervision with Post-Action Debt Accounting for Goal-Reaching RL
Jan 27, 2026Mehdi Heydari Shahna, Joongheon Kim, Jouni MattilaRL for Robotics Robot Safety
Faculty of Engineering and Natural Sciences, Tampere University, Tampere, Finland · Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea
AION: Aerial Indoor Object-Goal Navigation Using Dual-Policy Reinforcement Learning
Jan 22, 2026Zichen Yan, Yuchen Hou, Shenao Wang +3RL for Robotics Aerial Robotics
Department of Electrical and Computer Engineering, National University of Singapore, Singapore.
Humanoid Whole-Body Badminton via an Annealed Reinforcement Learning Curriculum
Nov 14, 2025Chenhao Liu, Leyun Jiang, Ningyuan Tian +4RL for Robotics Reinforcement Learning
Thor: Towards Human-Inspired Whole-Body Reactions for Intense Contact-Rich Environments
Oct 30, 2025Gangyang Li, Hongzhe Shi, Qing Shi +5RL for Robotics Humanoid Loco-Manipulation
Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology · Beijing Academy of Artificial Intelligence
CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
Oct 16, 2025Lizhi Yang, Blake Werner, Massimiliano de Sa +1RL for Robotics Control Barrier Functions
Caltech MCE
Pretraining in Actor-Critic Reinforcement Learning for Locomotion
Oct 14, 2025Jiale Fan, Andrei Cramariuc, Tifanny Portela +1RL for Robotics Reinforcement Learning
Section of Microengineering, EPFL, 1015 Lausanne, Switzerland · Robotic Systems Lab, ETH Zurich, 8092 Zürich, Switzerland · ETH AI Center, ETH Zurich, 8092 Zürich, Switzerland

May 12, 2026, cs.LGJ/K move · Enter open · S save

Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies

Alberta Longhini, David Emukpere, Jean-Michel Renders, Seungsu Kim

Department of Computer Science, Stanford University, CA, USA

RL for Robotics Reinforcement Learning RL for Generative Models RL Fine-Tuning

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We address the problem of fine-tuning pre-trained generative policies with reinforcement learning (RL) while preserving the multimodality of their action distributions. Existing methods for RL fine-tuning of generative policies (e.g., diffusion policies) improve task performance but often collapse diverse behaviors into a single reward-maximizing mode. To mitigate this issue, we propose an unsupervised mode discovery framework that uncovers latent behavioral modes within generative policies. The discovered modes enable the use of mutual information as an intrinsic reward, regularizing RL fine-tuning to enhance task success while maintaining behavioral diversity. Experiments on robotic manipulation tasks demonstrate that our method consistently outperforms conventional fine-tuning approaches, achieving higher success rates and preserving richer multimodal action distributions.

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