Synthetic Data Generation for Robotics

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

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RobotAPO: Adversarial Physics Preference Optimization for Robotic Manipulation Video Generation
Oct 7, 2026Kerui Li, Zhe Jing, Chenyi Huang +4Physical Consistency in Video Generation Robotic Manipulation
Institute of Automation, Chinese Academy of Sciences · GigaAI · Beijing Institute of Technology
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
PhysTacGen: Physics-Aware Visual-Tactile Sensor Image Generation
Oct 6, 2026Guo Tang, Yongtao WangPhysics-Informed Generative Modeling Tactile Sensing
Wangxuan Institute of Computer Technology, Peking University · VGI Labs Co., Ltd.
EmbodiedSmith: Scaling Embodied Data through Recursive Self-Improvement Flywheel in Simulation
Oct 6, 2026Yikai Qin, Yifei Deng, Mingjian Liang +13Robot Foundation Models Robotics Simulation
The Hong Kong University of Science and Technology (Guangzhou) · Xiaomi Robotics · Southeastern University +7
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
InterMimicGen: Scaling Humanoid Loco-Manipulation through Self-Evolving Motion Imitation
Oct 5, 2026Yucheng Zhang, Sirui Xu, Jinhong Li +8Humanoid Loco-Manipulation Synthetic Data Generation for Robotics
University of Illinois Urbana-Champaign · NVIDIA
Infant simulator with an embodied caregiver: Generating infant-perspective touch and vision during social interaction
Oct 5, 2026Noemi Vaculinova, Matej HoffmannRobotics Simulation Human Behavior Simulation
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
Bootstrapping Video Interaction Generation with Synthetic State Transitions
Oct 1, 2026Jiho Jang, Jinyoung Kim, Nojun Kwak +1Physical Consistency in Video Generation Synthetic Data Generation for Robotics
Seoul National University · Independent Researcher · Sejong University
Getting Out and Getting Back: World and Behavior Grounding in Real2Sim2Real Co-Training
Sep 30, 2026Samuel Liu, Youngsun Kim, Martin Matak +1Sim-to-Real Transfer Synthetic Data Generation for Robotics
University of Cambridge · Industrial Next
From Local Whole-Body VLA Behaviors to Scene-Scale Aerial Manipulation
Sep 30, 2026Weixiang Guo, Rui Jin, Haotian Jin +7Robotic Control Language-Conditioned Robot Manipulation
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · College of Electronics and Information Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 201804, China · School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China +1
PneuTac: Tactile Manipulation with Soft Pneumatic Robots via Unified MPM-Gaussian Splatting Simulation
Sep 29, 2026Shaohong Zhong, Marco Pontin, Joe Watson +2Robotics Simulation Synthetic Data Generation for Robotics
Oxford Robotics Institute, Department of Engineering Science, University of Oxford, OX1 3PJ Oxford, U.K.
Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
Sep 29, 2026Zihan Wang, Zhen Wu, Pieter Abbeel +6HOI Generation Humanoid Loco-Manipulation
Amazon FAR · UC Berkeley · Stanford +1
Exemplar2VQA: A Scalable Exemplar-Driven Visual Question Answering Generation Framework via Multi-Agent Coding
Sep 29, 2026Jiayu Ying, Qijian Tian, Ruijie Xu +4Visual Question Answering Multimodal Large Language Models
East China Normal University · Shanghai Jiao Tong University · Fudan University +1
RoboFin3D: A Sim-to-Real Platform for Robotic Surface Finishing
Sep 29, 2026Haowei Wen, Shangtao Li, Vaibhav Sanjay +3Robotics Simulation Sim-to-Real Transfer
Robotics Institute, School of Computer Science, Carnegie Mellon University
AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations
Sep 29, 2026Rui Huang, Yanlin Mu, Lidong Li +3Synthetic Data Generation for Robotics Vision-Language-Action Models
National University of Singapore · Beijing Institute of Technology
SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation
Sep 28, 2026He Zhu, Lusen Zhao, Kwan Man Cheng +2Robot Skill Learning Robotics Simulation
Carnegie Mellon University · University of Illinois Urbana-Champaign
DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Library
Sep 28, 2026Youhui Wang, Yunzhu Li, Li Fei-Fei +2Long-Horizon Robotic Manipulation Robotics Simulation
Stanford University · Columbia University
HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction
Sep 28, 2026Jihwan Shin, Adrià López Escoriza, Junzhe He +2Contact-Rich Robotic Manipulation Synthetic Data Generation for Robotics
Robotic Systems Lab, ETH Zürich, 8092 Zürich, Switzerland
NaviScale: Generating Large-Scale Semantic Map Datasets for Object Navigation
Sep 23, 2026Chuanlin Lan, Yanwei Zheng, Yuxi Jing +6Robot Navigation Synthetic Data Generation for Robotics
School of Computer Science and Technology, Shandong University, Qingdao, China
AgriGen: Large-Scale Scene Generation Framework for Photorealistic Agricultural Robotics Simulation
Sep 22, 2026Utkarsh Bajpai, Serge Tleiji, Cédric Pradalier +1Robotics Simulation Synthetic Data Generation for Robotics
Georgia Tech-CNRS IRL2958, France · Notre Dame University, Lebanon; work done during internship at 1
MotionForge: A Data Generation Pipeline and Large-Scale Benchmark for Long-Horizon Manipulation of Dynamic Objects with Domain Shifts
Sep 22, 2026Mohan Liu, Dengchen Mei, Haotian Xian +7Long-Horizon Robotic Manipulation Generalization in Robotic Manipulation
Embodied Perception and Interaction Lab (EmPACT Lab), School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, and the Agency for Science, Technology and Research (A*STAR), Singapore · Beijing Normal-Hong Kong Baptist University (BNBU), Zhuhai, Guangdong, China
PhyVisGen: Physically and Visually High-Fidelity Robotic Manipulation Data Generation
Sep 22, 2026Yu Zheng, Qiyu Feng, Yixin Wu +6Robotics Simulation Sim-to-Real Transfer
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China. · Global College, Shanghai Jiao Tong University, Shanghai, China. · Shenzhen Dizhou Technology Co., Ltd., Shenzhen, China +1
DynaForge: Planning-Guided Residual Learning for Dynamic Manipulation Demonstration Generation
Sep 22, 2026Yiyang Jin, Yu Zheng, Xiao He +1Synthetic Data Generation for Robotics Robot Motion Generation
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai 200240, China. · LandSpace Technology Co.,Ltd., China
Norm2Tex: Augmenting Visuo-Tactile Simulations with Texture
Sep 21, 2026Seongjin Bien, Débora Oliveira Makowski, Roberto Calandra +2Vision-Based Tactile Sensing Tactile Sensing
University of Technology Nuremberg, Germany. · Technical University of Dresden, Germany. · Deggendorf Institute of Technology, Germany.
Uranus: Building the Next-Generation Simulation Infrastructure for Embodied AI
Sep 21, 2026Wenkang Qin, Yukun Zhou, Noah Shen +5Robotics Simulation Synthetic Data Generation for Robotics
D-Robotics Large Model Team
ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation
Sep 21, 2026Bowei Li, Yuner Zhang, Changliu LiuRobotics Simulation Sim-to-Real Transfer
Carnegie Mellon University, Pittsburgh, PA 15213, USA
RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations
Sep 21, 2026Dorian Benhamou Goldfajn, Mason Nakamura, Saaduddin Mahmud +3Multi-Robot Systems Language-Conditioned Robot Manipulation
University of Massachusetts, Amherst, MA, USA. · University of California, Berkeley, CA, USA. · Northeastern University, Boston, MA, USA.
ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling
Sep 18, 2026Xuancheng Zhang, Xuetao Liu, Qianying Tang +8World Models for Robotics Visuo-Tactile Representation Learning
Foundation Model, Li Auto Inc.
DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real
Sep 17, 2026Jin Wu, Lianjie Yuan, Zeyan Sun +4Sim-to-Real Transfer Synthetic Data Generation for Robotics
The University of Texas at Austin · University of Florida · BrainCo
Towards Scaling Marine Perception with Synthetic Data
Sep 17, 2026Haoyu Ma, Onur Bagoren, Anja Sheppard +6Synthetic-to-Real Domain Adaptation Synthetic Data Generation
Department of Robotics, University of Michigan, Ann Arbor, MI, USA · Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI, USA

Oct 7, 2026, cs.ROJ/K move · Enter open · S save

RobotAPO: Adversarial Physics Preference Optimization for Robotic Manipulation Video Generation

Kerui Li, Zhe Jing, Chenyi Huang, Xiaofeng Wang+3

Institute of Automation, Chinese Academy of Sciences · GigaAI · Beijing Institute of Technology

Physical Consistency in Video Generation Robotic Manipulation Preference Optimization Synthetic Data Generation for Robotics

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Robotic manipulation videos are increasingly used as visual plans for embodied agents, but optimizing purely for visual plausibility often fails to capture the fragile physical manifold of real-world interactions. Even minor physics-violating errors at the interaction boundary, such as interpenetration or premature object motion, can completely invalidate the inferred timing and pose needed for downstream execution. Because standard supervised fine-tuning lacks the direct pressure to penalize these localized failures, we introduce AgiBot-PhysPref. This rigorously curated 10,000-sample preference dataset isolates condition-matched physics violations, turning the generator's own failure distribution into a foundational signal for physical consistency. Building upon this, we propose RobotAPO, an adversarial physics preference optimization framework operating in the continuous flow-matching denoising space. To prevent the policy from merely memorizing static curated failures, RobotAPO employs a lightweight adversarial counterfactual proposer that learns a condition-dependent, physical-failure-biased direction in denoising space. This encourages the model to explore and better respect the physical interaction boundary, all while maintaining a pure prompt-and-reference inference interface without requiring external structural conditioning. Comprehensive evaluations demonstrate that explicitly correcting these localized physics violations improves downstream robot execution from generated videos. On held-out AgiBot conditions, RobotAPO outperforms the strongest controlled internal baseline in physical consistency by 6.8% hard score and 10.0% soft score. Crucially, in real-robot replay, it translates these physical-consistency gains into a 37.4% relative improvement in task success over the strongest controlled internal baseline.

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