Synthetic Data Generation for Robotics

Follow this topic Ask about this topic

Momentum

40 papers in the last four weeks, up 567% on the four weeks before. 0.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 196

All topics

Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos
Jun 5, 2026Anurag Ghosh, Francesco Pittaluga, Khiem Vuong +4Autonomous Driving Datasets Autonomous Driving Benchmarks
1Carnegie Mellon University · 2NEC Labs America · 3MIT∗ +1
Task Editing for Generalizable 3D Visuomotor Policy Learning
Jun 5, 2026Jian-Jian Jiang, YiHan Yang, Lan Wei +6Long-Horizon Robotic Manipulation Generalization in Robotic Manipulation
Sun Yat-sen University · South China University of Technology · Nanyang Technological University +1
HomeWorld: A Unified Floorplan-to-Furnished Framework for Generating Controllable, Densely Interactive Whole-Home Scenes
Jun 4, 2026Wenbo Li, Xiaoliang Ju, Zipeng Qin +2Synthetic Data Generation for Robotics 3D Scene Layout Generation
Ace Robotics · CUHK MMLab · Shenzhen Loop Area Institute
Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation
Jun 4, 2026Ariel Herrera, Xueyang Kang, Atal Anil KumarBimanual Robotic Manipulation Deformable Object Manipulation
Department of Engineering, University of Luxembourg, Esch-sur-Alzette, Luxembourg · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Universit´e de Lorraine, Arts et Metiers Institute of Technology, LCFC, F-57070 Metz, France
Towards a Data Flywheel for Embodied Intelligence in Logistics
Jun 4, 2026Anlan Yu, Zaishu Chen, Zhiqing Hong +1Synthetic Data Generation for Robotics Robotics
Peking University Beijing, China · JD Logistics Beijing, China · HKUST (Guangzhou) Guangzhou, China
Learning of Robot Safety Policies via Adversarial Synthetic Scenarios
Jun 4, 2026Nikolai Dorofeev, Alexey Odinokov, Rostislav YavorskiyRobot Policy Learning Adversarial Scenario Generation
SafePI.ai Grenoble, France · SafePI.ai Belgrade, Serbia · SafePI.ai Madrid, Spain
GRAIL: Generating Humanoid Loco-Manipulation from 3D Assets and Video Priors
Jun 3, 2026Tianyi Xie, Haotian Zhang, Jinhyung Park +17HOI Generation Humanoid Loco-Manipulation
NVIDIA · UCLA
Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems
Jun 3, 2026R. Spencer Hallyburton, David Hunt, Miroslav PajicSynthetic Data Generation for Robotics Multi-Agent Systems
aDepartment of Electrical and Computer Engineering, Duke University, Durham, NC 27708 USA
Unified Video-Action Joint Denoising for Dexterous Action and Data Generation
Jun 2, 2026Dingrui Wang, YuAn Wang, Jinkun Liu +4Action-Conditioned Video Generation Video Diffusion Models
1Technical University of Munich · 2ByteDance · 3Tsinghua University
NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics
Jun 2, 2026Sicong Gao, Maurice Pagnucco, Tomasz Bednarz +1Robotics Simulation Synthetic Data Generation for Robotics
School of Computer Science and Engineering, The University of New South Wales · NVIDIA USA
RoboDream: Compositional World Models for Scalable Robot Data Synthesis
Jun 1, 2026Junjie Ye, Rong Xue, Basile Van Hoorick +6Robot Skill Learning Robotic Data Collection
1USC Physical Superintelligence (PSI) Lab · 2Toyota Research Institute
A Simulation Platform for Flapping-Wing Vehicles
Jun 1, 2026Haichuan Li, Tomi WesterlundAerial Robotics Robotics Simulation
University of Turku
Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX
May 31, 2026Martin Schuck, Marcel P. Rath, Yufei Hua +3Aerial Robotics Robotics Simulation
1Technical University of Munich · University of Toronto · 3Simon Fraser University
LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World
May 31, 2026Hojune Kim, Timothy Chen, Jiankai Sun +4Humanoid Loco-Manipulation Robotics Simulation
Stanford University
PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis
May 31, 2026Andrea Chianese, Giulio Rossolini, Alessandro Biondi +2Autonomous Driving Datasets Autonomous Driving Perception
Scuola Superiore Sant’Anna, Pisa, Italy, Department of Excellence in Robotics & AI · University of Pisa, DII, Pisa, Italy
SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models
May 30, 2026Ziheng He, Yixiang Chen, Ning Yang +11Synthetic Data Generation for Robotics Video Generation
1UCAS · 2CASIA · 3NJU +3
RDGen: Demonstration Generation for High-Quality Robot Learning via Reinforcement Learning
May 29, 2026Zijian Zhu, Menglin Zou, Zhuang Li +2RL for Robotics Robot Skill Learning
V2XCrafter: Learning to Generate Driving Scene Across Agents
May 28, 2026Yihang Tao, Yu Guo, Senkang Hu +4Autonomous Driving Synthetic Data Generation for Robotics
Hong Kong JC STEM Lab of Smart City · City University of Hong Kong · Lingnan University
HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning
May 26, 2026Kevin Lin, Ajay Mandlekar, Caelan Reed Garrett +7Humanoid Loco-Manipulation Synthetic Data Generation for Robotics
1NVIDIA · 2The University of Texas at Austin
Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data
May 26, 2026Marcus G Müller, Wout Boerdijk, Maximilian Durner +5Mobile Robotics Synthetic Data Generation for Robotics
Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Weßling, Germany · Federal Institute of Technology Zurich (ETH Zurich), Zurich, Switzerland · Robotics and AI Institute (RAI), Zurich, Switzerland
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
RS2AD-LiDAR: End-to-End Autonomous Driving LiDAR Data Generation from Roadside Sensor Observations
May 22, 2026Runyi Huang, Ni Ding, Ruidan Xing +3Synthetic Data Augmentation Autonomous Driving Perception
State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, 100084, China. · School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China. · College of Artificial Intelligence, Tsinghua University, Beijing, 100083, China. +2
Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving
May 21, 2026Jiahao Wang, Bo Sun, Yijing Bai +12Autonomous Driving Datasets Autonomous Driving
Waymo · Johns Hopkins University · Google DeepMind +1
PGDG: Physically Grounded Data Generation for Robust Bimanual Policy Learning from a Single Demonstration
May 20, 2026Cunxi Dai, Haoran Chang, Aditya Nisal +5Robot Policy Learning Bimanual Robotic Manipulation
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA · Dexmate, USA
SubTGraph: Large-Scale Subterranean Environment Synthesis with Controllable Topological Variability for Robotic Autonomy Validation
May 20, 2026F. Labra Caso, A. Saradagi, S. Fredriksson +3Robotics Simulation Synthetic Data Generation for Robotics
Robotics & AI Luleå University of Technology
FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model
May 19, 2026Jinhan Li, Xijie Huang, Weiqi Gai +9UAV Navigation Synthetic Data Generation for Robotics
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China. · Differential Robotics, Hangzhou 311121, China.
CosFly: Plan in the Matrix, Fly in the World
May 18, 2026Hanxuan Chen, Xiangyue Wang, Songsheng Cheng +8Trajectory Generation UAV Navigation
Autel Robotics · Nanjing University · Peking University +2
Guiding Neuro-Symbolic Scenario Generation with Spatio-Temporal Logic
May 18, 2026Lorenzo Bonin, Francesco Giacomarra, Luca Bortolussi +2Autonomous Driving Safety Evaluation Synthetic Data Generation for Robotics
University of Trieste · University of Southern California
CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization
May 18, 2026Xiangyue Wang, Hanxuan Chen, Songsheng Cheng +7Aerial Robotics Trajectory Optimization
Autel Robotics · Nanjing University · Peking University +2
Generating Realistic Safety-Critical Scenarios for Vehicle-Pedestrian Interactions
May 17, 2026Qingwen Pu, Kun Xie, Yuan Zhu +1Human Behavior Simulation Autonomous Driving Safety Evaluation
Transportation Informatics Lab, Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA 23529, United States · Inner Mongolia Center for Transportation Research, Inner Mongolia University, Rm A357A, Transportation Building, Inner Mongolia University South Campus, 49 S Xilin Rd, Hohhot, Inner Mongolia, 010020, China · School of Transportation and Logistics, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, National and Local Joint Engineering Research Center of Integrated Transportation Intelligence, Southwest Jiaotong University, Chengdu 611756, China

Jun 5, 2026, cs.CVJ/K move · Enter open · S save

Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos

Anurag Ghosh, Francesco Pittaluga, Khiem Vuong, Angela Chen+3

1Carnegie Mellon University · 2NEC Labs America · 3MIT∗+1

Autonomous Driving Datasets Autonomous Driving Benchmarks Autonomous Driving Dynamic Scene Reconstruction Synthetic Data Generation for Robotics Autonomous Driving Planning+2

Open paper AskPDF arXiv

Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios. Dashcam videos cover a far broader range of locations and situations, including rare or long-tailed scenarios. They are considered less usable for simulation because it is difficult to recover accurate 4D scenes from monocular in-the-wild videos. Work zones are one such class of long-tailed situations that dashcams capture. We present Dash2Sim, a framework that turns in-the-wild monocular dashcam videos into metric, geo-referenced 4D driving logs compatible with existing simulators, and verifies eachone against an independently maintained map without annotations. We apply Dash2Sim to a large video corpus to create the ROADWork4D benchmark dataset, which spans 4,244 scenes with 2.7M 3D objects across 17 cities. On a verified subset ROADWork4D-CL (2,201 scenes), we study privileged closed-loop planners and find that work zone scenarios are difficult: while rule-based and hybrid planners generalize better than learning-based ones, all fall short, failing to make the lane changes that temporary work zone channels require. Beyond planning, dense depth recovered by Dash2Sim improves novel-view synthesis quality by up to 19% on perceptual metrics, suggesting its potential to provide rich conditioning for closed-loop sensor simulation from monocular videos.

Momentum

Related topics

Latest papers 196