Robotic RL

RL: Reinforcement Learning

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28 papers in the last four weeks, up 180% on the four weeks before. 0.3% of all new papers.

Jul 13Week of Sep 28

Latest papers 184

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FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation
Jun 21, 2026Kexin Shi, Junyao Shi, Poorvi Hebbar +5Flow Matching Policy Gradient
Skild AI · Carnegie Mellon University · University of Pennsylvania
RARM: Confidence-Gated Progress Reward Modeling for RL in Manipulation
Jun 20, 2026Pengzhi Yang, Xinyu Wang, Pengyu Jing +7Long-Horizon Robotic Manipulation Robot Skill Learning
† NUS Human-Centered Robotic Lab · ‡ Booking.com · § University of Cambridge +1
Backpropagating Through Simulation: Analytic Policy Gradients for Sample and Learning Efficient Differentiable Continuous Control
Jun 19, 2026Yueci DengRobotic RL Policy Gradient Methods
School of Data Science, The Chinese University of Hong Kong, Shenzhen
A Reward-Petri-Net Interpretation of Temporal Behavior Trees
Jun 19, 2026Till Schmeil, Günther Waxenegger-Wilfing, Sebastian SchirmerReinforcement Learning Reward Shaping
University of Würzburg, Germany · German Aerospace Center (DLR), Germany
DF-ExpEnse: Diffusion Filtered Exploration for Sample Efficient Finetuning
Jun 17, 2026Calvin Luo, Chen Sun, Shuran SongRobot Policy Adaptation Robotic RL
1Stanford University · 2Brown University.
Recover, Discover, Plan: Learning Skills and Concepts from Robot Failures
Jun 16, 2026Bowen Li, Mayank Mishra, Y. Isabel Liu +7Robot Skill Learning Generalization in Robotic Manipulation
CMU · Centaur AI · Princeton +3
Hierarchical Advantage Weighting for Online RL Fine-Tuning of VLAs from Sparse Episode Outcomes
Jun 15, 2026Tongyan Fang, Siyuan Huang, Naiyu Fang +6Credit Assignment in RL Robotic RL
1ACE Robotics · 2Shenzhen International Graduate School, Tsinghua University · 3The Chinese University of Hong Kong
Task-Error Residual Learning for Real-Robot Five-Ball Juggling
Jun 15, 2026Kai Ploeger, Jan PetersRobotic Control Robot Skill Learning
Technical University of Darmstadt, Germany · German Research Center for AI (DFKI), Germany · Hessian Center for Artificial Intelligence (hessian.AI), Germany
Steering Generative Reinforcement Learning into Stable Robotic Controller
Jun 15, 2026Yixuan Wang, Shutong Ding, Ke Hu +3Latent Action Learning Robotic RL
1ShanghaiTech University
Agile Fall Recovery for Quadrotors with Bidirectional Thrust via Reinforcement Learning
Jun 15, 2026Anke Zhao, Yuhang Zhong, Kenghou Hoi +5Robot Failure Recovery Robotic RL
Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · Differential Robotics, Hangzhou 311121, China
TopoRetarget: Interaction-Preserving Retargeting for Dexterous Manipulation
Jun 15, 2026Jielin Wu, Shenzhe Yao, Guanqi He +6Robot Skill Learning Contact-Rich Robotic Manipulation
IIIS, Tsinghua University
Energy-Efficient Arm Reaching for a Humanoid Robot via Deep Reinforcement Learning with Identified Power Models
Jun 14, 2026Nestor N. Deniz, Simon Parsons, Fernando Auat CheeinRobotic Control Humanoid Robot Control
Engineering Department at Harper Adams University, Newport, Shropshire TF10 8NB, UK · Lincoln Institute for Agri-Food Technology and Lincoln Centre for Autonomous Systems
Provably Safe, Yet Scalable Reinforcement Learning
Jun 12, 2026Kai S. Yun, Zeyang Li, Navid AzizanRL Control Safety-Critical Control
MIT
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
Jun 12, 2026He Zhang, Lingzhu Xiang, Haitao Lin +23Robotic RL Vision-Language-Action Models
Tencent Robotics X × Tencent Hy Team
UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning
Jun 10, 2026Haoyuan Deng, Yitong Gao, Yudong Lin +3Agentic RL Reinforcement Learning
Nanyang Technological University · Beijing University of Posts and Telecommunications
KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility
Jun 10, 2026Zhiming Xu, Haodong Yang, Chengju Liu +2Kinodynamic Motion Planning Sim-to-Real Transfer
School of Computer Science and Technology, Tongji University, Shanghai 201804, China · Department of Electronics and Information Engineering, Tongji University, Shanghai 201804, China · Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201210, China
PAWS: Preference Learning with Advantage-Weighted Segments
Jun 10, 2026Aleksandar Taranovic, Onur Celik, Niklas Freymuth +6Credit Assignment in RL Robotic RL
Autonomous Learning Robots, Karlsruhe Institute of Technology, Karlsruhe, Germany · Bosch Center for Artificial Intelligence, Renningen, Germany · Institute for Material Handling and Logistics (IFL), Karlsruhe Institute of Technology, Karlsruhe, Germany
Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
Jun 9, 2026Yuchi Zhao, Miroslav Bogdanovic, Arjun Sohal +5Robot Policy Learning Long-Horizon Robotic Manipulation
Department of Computer Science, University of Toronto, 40 St George St., Toronto, ON M5S 2E4, Canada · Vector Institute for Artificial Intelligence, W1140-108 College St., Schwartz Reisman Innovation Campus, Toronto, ON M5G 0C6, Canada · Acceleration Consortium, 700 University Ave., Toronto, ON M7A 2S4, Canada +7
ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration
Jun 8, 2026Yuying Zhang, Francesco Verdoja, Wenyan Yang +1Robot Skill Learning Robotic RL
School of Electrical Engineering Aalto University, Finland
IR-SIM: A Lightweight Declarative Simulator for Navigation Learning and Benchmarking
Jun 7, 2026Ruihua Han, Shuai Wang, Chengyang Li +8Robotics Simulation Robot Navigation
The University of Hong Kong · Shenzhen Institutes of Advanced Technology · Southern University of Science and Technology +2
HARBOR: A Harness Framework for Agentic Robot Reinforcement Learning
Jun 7, 2026Zechu Li, Yufeng Jin, Xiaoyang Liu +4Agentic RL Reinforcement Learning
TU Darmstadt 1 · Hessian.AI 7 · Honda Research Institute Europe 2 +4
Reinforcement Learning for Flow-Matching Policies with Density Transport
Jun 7, 2026Boshu Lei, Kostas Daniilidis, Antonio LoquercioFlow Matching Robotic RL
University of Pennsylvania · Philadelphia, PA 19103
ActProbe: Action-Space Probe for Early Failure Detection of Generative Robot Policies
Jun 7, 2026Bingjia Huang, Xiangyu Li, Xiang Wang +7Robot Policy Learning Robot Failure Detection
Institute for AI Industry Research (AIR), Tsinghua University · University of Electronic Science and Technology of China · Work done during internship at AIR, Tsinghua University. +1
Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
Jun 5, 2026Arthur Bouton, Tristan D. Hasseler, Michael Paton +5Off-Road Robot Navigation Robotic RL
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA · Center for Autonomy, University of Texas at Austin, Austin, TX 78712 USA · Space Systems Laboratory, University of Maryland, MD 20742 USA
L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation
Jun 4, 2026Liwen Zhang, Dong Zhou, Guanghui Sun +4Space Robotics Robotic RL
Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
MoDex: A Diffusion Policy for Sequential Multi-Object Dexterous Grasping
Jun 3, 2026Haofei Lu, Hongjia Liu, Yifei Dong +3Robotic Grasping Robotic RL
Department of Robotics, Perception and Learning, KTH Royal Institute of Technology, Sweden. · Robotics and Autonomous Systems at University of Turku, Finland.
Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation
Jun 2, 2026Zeyi Liu, Guangyao Liu, Yinuo Qu +6Credit Assignment in RL Robotic RL
Central South University · Nanyang Technological University · Zhejiang University
GN0: Toward a Unified Paradigm for Generation, Evaluation, and Policy Learning in Visual-Language Navigation
Jun 2, 2026Xinhai Li, Xiaotao Zhang, Yuehao Huang +10Robot Policy Learning Embodied Navigation
Institute of Artificial Intelligence, China Telecom · Shanghai Jiao Tong University · Zhejiang University +3
A GPU-Parallel Framework for Heterogeneous Multi-Task Reinforcement Learning
Jun 2, 2026Rui Zhang, Qiwei Wu, Zhengyu Zhang +7Robotic RL Learning from Demonstration
NVIDIA · The Hong Kong University of Science and Technology (Guangzhou)
Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
Jun 1, 2026Zhiming Xu, Weitao Zhou, Xianghui Pan +4Robot Policy Adaptation Latent Dynamics Modeling
School of Computer Science and Engineering, Tongji University, Shanghai, China · School of Vehicle and Mobility, Tsinghua University, Beijing, China · Simple AI, Beijing, China +1

Jun 21, 2026, cs.ROJ/K move · Enter open · S save

FlowDPG: Deterministic Policy Gradient on Flow Matching Policies for Real-World Manipulation

Kexin Shi, Junyao Shi, Poorvi Hebbar, Zhuolun Zhao+4

Skild AI · Carnegie Mellon University · University of Pennsylvania

Flow Matching Policy Gradient Long-Horizon Robotic Manipulation Robotic RL Robotic Manipulation Deep Deterministic Policy Gradient

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Real-world reinforcement learning for robotic manipulation remains challenging, and this difficulty is amplified for flow matching policies: policy gradients must be backpropagated through time (BPTT) along the multi-step ODE that maps noise to actions, which is computationally prohibitive and numerically fragile. We propose FlowDPG, a DDPG-style method for flow matching policies that bypasses BPTT entirely. FlowDPG evaluates the critic gradient at a one-step estimate of the clean action and distills the resulting correction into the velocity field at training time, leaving multi-step inference unchanged. Intuitively, it combines two complementary vectors: the demonstration-driven velocity that keeps the action feasible, and the critic-driven correction that steers it toward higher value. Our contributions are threefold: (1) a BPTT-free distillation framework for stable DDPG-style improvement of flow matching policies, (2) a formal connection between the FlowDPG update and the vanilla deterministic policy gradient via three explicit approximations, and (3) real-world validation on two long-horizon tasks on different robots. FlowDPG reaches 92% end-to-end success on dual-arm AirPods assembly with Franka arms and an 86% rubric score on scrambled-egg cooking with YAM arms, substantially outperforming recent RL methods. Videos and more results: https://flowdpg.github.io.

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Latest papers 184