Sim-to-Real Transfer

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Latest papers 221

56 papers in the last four weeks, up 250% on the four weeks before. 0.4% of all new papers.

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Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
Oct 8, 2026Jusuk Lee, Sungha Kim, Yeonsoo Park +8Robotic Manipulation Learning from Demonstration
Seoul National University · University of Maryland, College Park · All Purpose AI +1
Sim-to-Real RL for ASVs using SysID
Oct 8, 2026Cody Sheltraw, Tsimafei Lazouski, Maani Ghaffari +1Sim-to-Real Transfer Robotics Simulation
University of Michigan, Ann Arbor, MI 48109, USA.
Onboard Marine Anomaly Detection on $Φ$ sat-2: From Simulation-Based Development to In-Orbit Demonstration
Oct 8, 2026Clotilde Szywala, Thomas Goudemant, Marjorie Bellizzi +2Anomaly Detection Remote Sensing Image Understanding
Institut de Recherche Technologique Saint Exupéry 240 Rue Evariste Galois 06410 Biot, France
SimVLA: Zero-Shot Sim-to-Real VLA Learning for Mobile Manipulation
Oct 8, 2026Kyoungin Baik, Youngwoon LeeSim-to-Real Transfer Vision-Language-Action Models
Yonsei University · Seoul National University
Informationally Decoupled Trajectory Design for Sim-to-Real System Identification
Oct 7, 2026Sangwoo Shin, Ashvin Anilkumar, Ryan Gao +1Robot Dynamics Identification Parameter Identifiability
University of Wisconsin–Madison
Factorized Tactile Representation and Control for Sim-to-Real Manipulation
Oct 7, 2026Siqi Shang, Bianca Aumann, Tye Brady +2Sim-to-Real Transfer Tactile Sensing for Robotic Manipulation
Amazon Fulfillment Technologies & Robotics, Westborough, MA, USA. · Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies
Oct 7, 2026Yihan Li, Yating Feng, Shengjiu Sun +7Sim-to-Real Transfer Robotic Manipulation
Sun Yat-sen University · The Hong Kong University of Science and Technology (Guangzhou)
OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework
Oct 7, 2026Yifan Wu, Qin Li, Nan Min +14Robot Manipulation Benchmarks Tactile Sensing for Robotic Manipulation
Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Hefei University of Technology +3
Point It, Strike It: Direction-Conditioned Dynamic Manipulation of Deformable Linear Objects
Oct 7, 2026Yi Yang, Xiang Fei, Lehong Wang +9Deformable Object Manipulation Dynamic Robotic Manipulation
The Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213 USA. · School of Ocean and Civil Engineering, Shanghai Jiao Tong University, 800 Dongchuan Rd, Minhang District, Shanghai, China. · Zhiyuan College, Shanghai Jiao Tong University, 800 Dongchuan Rd, Minhang District, Shanghai, China. +1
RoboRender: Robot-Oriented Video Generation for Visual Sim-to-Real Transfer
Oct 7, 2026Huang Huang, Wensi Ai, Ziyu Chen +6Sim-to-Real Transfer Synthetic Data Generation for Robotics
Stanford University · Nvidia · Microsoft Research
AdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model
Oct 6, 2026Sarim Hashmi, Mukul Ranjan, Kshitij Mishra +3Adversarial Training Sim-to-Real Transfer
Mohamed bin Zayed University of Artificial Intelligence · Amazon · Massachusetts Institute of Technology
RenderBench: Benchmarking Render-to-Real Video Transfer with Reconstructed Digital Twins
Oct 6, 2026Dicong Qiu, Zhiyuan Xu, Yaosheng Liu +2Sim-to-Real Transfer Digital Twins
The Hong Kong University of Science and Technology (Guangzhou) · Southeast University · Independent Researcher
Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction
Oct 6, 2026Hojun Lim, Hyeongseok Jeon, Donghyun Kim +2Synthetic Data Augmentation Sim-to-Real Transfer
Technical University of Munich, Germany; School of Engineering & Design, Department of Mobility Systems Engineering, Institute of Automotive Technology and Munich Institute of Robotics and Machine Intelligence (MIRMI) · MORAI Inc., Seoul, South Korea · AVP Division, Hyundai Motor Company, Gyeonggi-do, South Korea +1
SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining
Oct 6, 2026Jicong Ao, Shuhan Jiang, Yuling Zhong +7Robotics Simulation Sim-to-Real Transfer
Institute of Artificial Intelligence, China Telecom · Zhejiang University · Technical University of Munich +4
Learning Grasp Targeting from Point Clouds for Log Pile Clearing on a Hydraulic Crane
Oct 6, 2026George Sideris, Lucas Bessai, Heshan Fernando +3Robotic Grasping Sim-to-Real Transfer
McGill University, Montreal, QC, Canada. · FPInnovations, Pointe-Claire, QC, Canada.
ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction
Oct 5, 2026Jinzhou Li, Hadi Tabatabaee, Kelin Yu +6Sim-to-Real Transfer Robotic Manipulation
Amazon · Duke University · University of Maryland, College Park +2
RACER: Residual-Adaptive Closed-Loop Estimation for Sampling-Based Planning in Wheeled-Quadruped Racing
Oct 5, 2026Yuxiang Liu, Marla Eisman, Lizhi Yang +2Residual Dynamics Learning Sim-to-Real Transfer
University of California, Berkeley, CA, USA. · California Institute of Technology, Pasadena, CA, USA.
Sim-to-Real Transfer of Vision-Language Navigation in Continuous Environments Using an Ackermann-Steered Mobile Robot
Oct 5, 2026Chalindu Abeywansa, Sahan Gunasekara, Devindi De Silva +3VLM Adaptation Sim-to-Real Transfer
Department of Electronic and Telecommunication Engineering, University of Moratuwa, Colombo, Sri Lanka.
SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
Oct 5, 2026Xiaodong Wang, Tianle Li, Chuanxin Song +4Action-Conditioned Video Generation World Models for Robotics
Peking University
Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation
Oct 5, 2026Álvaro Díez, Fidel AznarVariational Autoencoders Domain Adaptation
Department of Computer Science and Artificial Intelligence, University of Alicante
Robust Surgical Robotic Instrument Tracking via Sequential Multi-Cue Fusion and Sim-to-Real Self-Training
Oct 4, 2026Hanyang Hu, Zekai Liang, Florian Richter +1Self-Training Sim-to-Real Transfer
Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093 USA.
TUCO: Curating Simulation Demonstrations for Sim-to-Real Robot Policy Co-Training
Oct 4, 2026Ning Zhu, Mengfei Zhao, Yikai Tang +5Robot Policy Learning Sim-to-Real Transfer
Stanford University · Nanyang Technological University · AXIS Robotics +4
VideoResearchAgent: Grounded Task Synthesis and Sim-to-Real RL for Open-Web Video Research
Oct 4, 2026Yuhang Zhou, Fei Li, Yuxi Wu +2Autonomous Research Agents Video Reasoning
Institute of Trustworthy Embodied AI, Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Singapore Management University
Tackling Sim-to-Real Mismatch Through Sampling-Based Disturbance Observers: From Analytical Models to Learned World Models
Oct 4, 2026Tianqi Zhu, Jun Yang, Jianliang Mao +2Robotic Control Sim-to-Real Transfer
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University, Nanjing 210096, China · Robotics and Autonomous Systems Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China · Faculty of Artificial Intelligence, Shanghai University of Electric Power, Shanghai 200090, China
Forecasting from Counterfactual Simulator Rollouts: A Sim2Real Evaluation
Oct 2, 2026Angel Wang, Dominique Perrault-Joncas, Alvaro Maggiar +2Sim-to-Real Transfer Time Series Forecasting
Amazon
SARI: Phase-Split Sim-Real Co-Training for Contact-Rich Manipulation
Oct 2, 2026Xingxin He, Yuxuan Jiang, Haonan Zhang +5Contact-Rich Robotic Manipulation Sim-to-Real Transfer
The Hong Kong University of Science and Technology · BYD Company Limited
Skill2Real: Agentic Skill Learning for Zero-Shot Sim-to-Real Robot Manipulation
Oct 2, 2026Xincheng He, Siyu Ma, Chang Yu +5Robot Skill Learning Sim-to-Real Transfer
University of California, Los Angeles · Shanghai Jiao Tong University · University of Utah +1
RoboBridge: A Self-Evolving Embodied Agent Framework for Sim-to-Real Transfer
Oct 2, 2026Chenxi Li, Zhangrui Zhao, Rui Li +8Sim-to-Real Transfer Robot Policy Adaptation
Shanghai Artificial Intelligence Laboratory · Zhejiang University · Beihang University +3
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Oct 1, 2026Yen-Jen Wang, Haozhe Jiang, Shuying Deng +7Robot Skill Learning Large Language Model-Based Robot Planning
UC Berkeley · Amazon FAR · MIT +1
Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems
Oct 1, 2026Mahindra Rautela, Alexander Scheinker, Ayan Biswas +3Sim-to-Real Transfer PDE Surrogate Modeling
Los Alamos National Laboratory, New Mexico, USA

Oct 8, 2026, cs.ROJ/K move · Enter open · S save

Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration

Jusuk Lee, Sungha Kim, Yeonsoo Park, Jonguk Cheon+7

Seoul National University · University of Maryland, College Park · All Purpose AI+1

Robotic Manipulation Learning from Demonstration Zero-Shot Robotic Manipulation RL for Robotics Reinforcement Learning Sim-to-Real Transfer

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While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions. Such strict motion matching often limits generalization to initial object poses, goal poses, and grasps not shown in the video. Alternatively, discovering a policy via reinforcement learning (RL) allows for broad generalization, but without prior guidance, it struggles with high-dimensional exploration in complex, multi-stage tasks. To address these coupled generalization and exploration challenges, we present Dex-One2Many, a real-to-sim-to-real framework that learns a generalizable dexterous manipulation policy from a single human video. Our key insight is to abstract the video into sequential scene graphs that guide RL, enabling efficient exploration while preserving broad generalizability. The graphs serve as generative constraints for sampling diverse reset states and provide dense rewards for each stage. Because the graphs constrain relations rather than exact poses, these reset states cover object poses and grasps beyond the video, while initializing each stage from them with dense rewards keeps exploration short and guided. Trained entirely in simulation, Dex-One2Many transfers zero-shot to a real multi-fingered hand. Across five tool-use and manipulation tasks, Dex-One2Many exceeds baselines by 6.5% in seen configurations, while its robust generalization widens this gap to 71% in unseen scenarios.