Sim-to-Real Transfer

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

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TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Jun 10, 2026Siyu Ma, Yuqi Liang, Chang Yu +5RL for Robotics Robot Skill Learning
University of California, Los Angeles · University of California, San Diego · 4Peking University +1
Bridging the sim2real gap in the table tennis robot with a transformer-based ball states predictor
Jun 9, 2026Yin Bi, Christian Conti, Bilan Yang +3Robot Policy Learning Sim-to-Real Transfer
Sony AI, Zürich, Switzerland · Sony AI, Tokyo, Japan
From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting
Jun 9, 2026Woojung Son, Won Suk Lee, Zijing Huang +4Sim-to-Real Transfer Object Pose Estimation
Department of Agricultural and Biological Engineering, University of Florida · Department of Electrical and Computer Engineering, University of Florida · Edwardson School of Industrial Engineering, Purdue University +1
A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
Jun 9, 2026Shuo Wang, Hanyuan Xu, Yingdong Hu +2Robotics Simulation Sim-to-Real Transfer
Tsinghua University · Shanghai Qi Zhi Institute
Autonomous Obstacle Removal for Excavators through Policy Learning with Particle Simulation
Jun 8, 2026Yuki Kadokawa, Sandro M. Alcantara Tacora, Taro Abe +4Robot Policy Learning Sim-to-Real Transfer
Nara Institute of Science and Technology · Public Works Research Institute, Ibaraki 300-2621, Japan
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Jun 7, 2026Yunhai Han, Jianuo Qiu, Linhao Bai +14Robot Skill Learning Sim-to-Real Transfer
1Georgia Institute of Technology · University of Pennsylvania · 3Toyota Research Institute +1
OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation
Jun 7, 2026Zehao Yu, Jiakun Zheng, Weiji Xie +4Humanoid Loco-Manipulation Sim-to-Real Transfer
Institute of Artificial Intelligence (TeleAI), China Telecom · Fudan University · East China University of Science and Technology +1
SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation
Jun 6, 2026Songlin Wei, Zhenhao Ni, Jie Liu +9Humanoid Loco-Manipulation Robotics Simulation
USC Physical Superintelligence (PSI) Lab
Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR
Jun 6, 2026Kerri Prinos, Lilianne BrushAutonomous Cyber Defense Sim-to-Real Transfer
Horizon3.ai San Francisco, CA, USA
QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation
Jun 5, 2026Yuxiang Chen, Yuanhao Wang, Ziheng Zhang +6Robotics Simulation Sim-to-Real Transfer
1Nanjing University · 2BUPT · 3DEXMAL +1
The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective
Jun 5, 2026Xiaoou Liu, Tiejin Chen, Weibo Li +2Sim-to-Real Transfer AI Agent Evaluation
Arizona State University Tempe, Arizona, USA
Generalization of World Models under Environmental Variability for Vision-based Quadrotor Navigation
Jun 3, 2026Luca Zanatta, Grzegorz Malczyk, Kostas AlexisWorld Models for Robotics World Models
Norwegian University of Science and Technology
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
May 31, 2026Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20Sim-to-Real Transfer Physics-Based Simulation
University of Cambridge, UK · Polymathic AI · CEA, DAM/DIF, 91297 Arpajon Cedex, France +6
Too Much of a Good Thing: When sim2real Efforts Impede Policy Learning (And What to Do About It)
May 30, 2026Kyle Morgenstein, Bharath Masetty, Stephen Welch +1Robot Policy Learning Robotics Simulation
Apptronik · University of Texas at Austin
DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
May 29, 2026Oussama Zaim, Mélodie Daniel, Aly Magassouba +2Robotic Control Robotics Simulation
Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France. · School of Computer Science, University of Nottingham, UK. · Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.
The Challenges of Using Reinforcement Learning for Controlling Industrial Energy Systems
May 29, 2026Tobias Lademann, Théo Vincent, Jan Peters +1RL Control Partially Observable RL
Institute for Production Management, Technology and Machine Tools (PTW), Technical University of Darmstadt · DFKI GmbH, SAIROL · Department of Computer Science, Technical University of Darmstadt +1
Prior Availability in Industrial Visual Sim-to-Real: A Review of CAD-Guided and CAD-Unavailable Regimes
May 28, 2026Chenxi Tao, Seung-Kyum ChoiSynthetic-to-Real Domain Adaptation Sim-to-Real Transfer
George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology
Bridging the Sim-to-Real Gap in Reinforcement Learning-Based Industrial Dispatching through Execution Semantics
May 27, 2026Jonathan Hoss, Noah KlarmannReinforcement Learning Sim-to-Real Transfer
Department of Industrial Engineering
Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning
May 27, 2026Christoph Dann, Yishay Mansour, Mehryar MohriSim-to-Real Transfer Active Learning
Google Research · Tel Aviv University and Google Research · Google Research and Courant Institute of Mathematical Sciences
Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation
May 27, 2026Jiahe Pan, Stelian Coros, Jitendra Malik +1RL for Robotics Tactile Sensing
1ETH Zürich · 2UC Berkeley
Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment
May 26, 2026Gengyue Han, Yiheng FengReinforcement Learning Sim-to-Real Transfer
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, USA · Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, USA
HyperSim: A Holistic Sim-To-Real Framework For Robust Robotic Manipulation
May 26, 2026Junyi Dong, Haotian Luo, Ziwei Xu +11Synthetic-to-Real Domain Adaptation Sim-to-Real Transfer
CloudRobo Lab, Huawei Cloud Computing Technologies Co.,Ltd. · Shanghai Jiao Tong University · The University of Hong Kong
Towards Real-World Identification of Fatigued Muscle Groups via Musculoskeletal Simulation
May 23, 2026Jenishkumar Chauhan, Samarth Brahmbhatt, Vineet VashistaMedical Diagnosis Sim-to-Real Transfer
Human-Centered Robotics Lab at IIT Gandhinagar, Gujarat, India · independent researcher from Mountain View, California, USA
MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy
May 21, 2026Jiaxu Wang, Junhao He, Jingkai Sun +5Residual Learning Sim-to-Real Transfer
Hong Kong University of Science and Technology, Guangzhou, China · MMLab, Chinese University of Hong Kong, Hong Kong SAR · The University of Hong Kong, Hong Kong SAR
SADGE: Structure and Appearance Domain Gap Estimation of Synthetic and Real Data
May 21, 2026Patryk Bartkowiak, Bartosz Kotrys, Dominik Michels +2Domain Adaptation Sim-to-Real Transfer
Adam Mickiewicz University · ArtCollect · KAUST +1
CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
May 21, 2026Wentian Wang, Chutong Wen, Hongxu Ma +6Robot Policy Adaptation Robot Skill Learning
Synthoid AI · HKU
Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
May 20, 2026Alessandro Amici, Houari Bettahar, Veeti Jaakkola +1Sim-to-Real Transfer Deformable Object Manipulation
Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Mind the Sim-to-Real Gap & Think Like a Scientist
May 20, 2026Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1Sim-to-Real Transfer RL Exploration
Amazon SCOT, Seattle, USA · Yale University, New Haven, USA · Duke University, Durham, USA
Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
May 20, 2026Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6Sim-to-Real Transfer Point Cloud Learning
1Autonomous Learning Robots · 2Methods for Big Data · Institute of Vehicle System Technology
Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction
May 19, 2026Zongwu Xie, Yonglong Zhang, Yifan Yang +2Synthetic-to-Real Domain Adaptation Synthetic Data Generation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

Jun 10, 2026, cs.ROJ/K move · Enter open · S save

TacCoRL: Integrating Tactile Feedback into VLA via Simulation

Siyu Ma, Yuqi Liang, Chang Yu, Yunuo Chen+4

University of California, Los Angeles · University of California, San Diego · 4Peking University+1

RL for Robotics Robot Skill Learning Contact-Rich Robotic Manipulation Sim-to-Real Transfer Vision-Language-Action Models Tactile Sensing for Robotic Manipulation

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Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks. We present TacCoRL, a scalable framework that injects Tactile feedback into VLA policies and improves them through sim-real Co-training and simulation-based reinforcement learning (RL), without requiring large-scale tactile pretraining or extensive real-world contact exploration. The key idea is not only adding touch as an input, but learning how contact readings should modulate action responses in near-failure states that are rare in demonstrations and risky to collect on hardware. We use a real-aligned simulator as a closed-loop training environment for contact interaction. Mixed simulated and real trajectories first warm-start tactile-conditioned actions in the pretrained policy. Reinforcement learning with verifiable task rewards then optimizes the policy using simulated contact rollouts. It reinforces tactile-conditioned actions that lead to task completion, while a supervised objective on real trajectories keeps the refined policy anchored to deployment visual, tactile, and action distributions. The resulting policy transfers directly to the real robot without privileged simulation state or online real-world RL. Across four bimanual contact-rich tasks, the final visuo-tactile policy achieves an average success rate of 72.5%, compared to baseline of 50.0%. Result videos and more details are available at https://tac-corl.github.io/