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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ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles
Feb 12, 2026Seungyeon Yoo, Youngseok Jang, Dabin Kim +3Robotics Simulation Robot Navigation
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea · InnoCORE AI-Transformed Aerospace Research Center, KAIST, Daejeon, Republic of Korea
MobileManiBench: Simplifying Model Verification for Mobile Manipulation
Feb 5, 2026Wenbo Wang, Fangyun Wei, QiXiu Li +5Mobile Manipulation Robotics Simulation
University of Sydney · 1Microsoft Research Asia · 3Tsinghua University
Robotic Video World Models: A Survey of Applications, Research Challenges, Future Directions
Jan 12, 2026Zhiting Mei, Tenny Yin, Ola Shorinwa +9Physical Consistency in Video Generation World Models for Robotics
Princeton University · Temple University
Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
Jan 5, 2026Chenghao Yin, Da Huang, Di Yang +16Robotic Data Collection Robotics Simulation
AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis
Dec 12, 2025Junjie Ye, Rong Xue, Basile Van Hoorick +4Video Diffusion Models Synthetic Data Generation for Robotics
Toyota Research Institute · USC Physical Superintelligence (PSI) Lab
MIND-V: Hierarchical World Model for Long-Horizon Robotic Manipulation with RL-based Physical Alignment
Dec 7, 2025Ruicheng Zhang, Mingyang Zhang, Jun Zhou +6Physical Consistency in Video Generation Long-Horizon Robotic Manipulation
Tsinghua University · Sun Yat-sen University · X Square Robot +1
TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation
Dec 1, 2025Ziqian Wang, Yonghao He, Licheng Yang +6Robotic Data Collection Robotics Simulation
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences, Beijing, China · D-Robotics, Beijing, China +2
FailSafe: Reasoning and Recovery from Failures in Vision-Language-Action Models
Oct 2, 2025Zijun Lin, Jiafei Duan, Haoquan Fang +4Robot Failure Recovery Synthetic Data Generation for Robotics
1Nanyang Technological University · Centre for Frontier AI Research, A*STAR · 3Allen Institute for AI +1
Learning from Hallucinating Critical Points for Navigation in Dynamic Environments
Sep 30, 2025Saad Abdul Ghani, Kameron Lee, Xuesu XiaoTrajectory Generation Synthetic Data Generation for Robotics
George Mason University · Thomas Jefferson High school intern affiliated with the RobotiXX Lab, George Mason University
Self-CriTeach: LLM Self-Teaching and Self-Critiquing for Improving Robotic Planning via Automated Domain Generation
Sep 25, 2025Jinbang Huang, Zhiyuan Li, Yuanzhao Hu +4Large Language Model-Based Robot Planning Synthetic Data Generation for Robotics
Huawei Noah’s Ark Lab · University of Toronto · University of British Columbia +1
A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting
Sep 18, 2025Xingyu Chen, Hanyu Wu, Sikai Wu +7Humanoid Robot Control Synthetic Data Generation for Robotics
1HKUST(GZ) · 4Xiaomi Robotics Lab · 2ETH Zurich +1
Affogato: Open-Vocabulary Affordance Grounding with Automated Data Generation at Scale
Jun 13, 2025Junha Lee, Eunha Park, Chunghyun Park +2Synthetic Data Generation for Robotics Embodied AI
Pohang University of Science and Engineering (POSTECH) · SqueezeBits · RLWLRD
ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation
May 26, 2025Dongyu Luo, Kelin Yu, Amir-Hossein Shahidzadeh +3Vision-Based Tactile Sensing Synthetic Data Generation for Robotics
The University of Hong Kong · University of Maryland · University of Maryland, College Park
Simulating Automotive Radar with Lidar and Camera Inputs
Mar 11, 2025Peili Song, Dezhen Song, Yifan Yang +2Synthetic Data Augmentation Millimeter-Wave Radar
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300350, China; Tianjin Key Laboratory of Intelligent Robotics, Tianjin 300350, China; and also with TBI center, Nankai University, Tianjin 300350, China · Department of Robotics, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
GIF: Agentic Generation of Interactive and Functional Object Compositions for Robot Learning
Date pendingLong Xu, Zhiqi Zhang, Mi Yan +8Synthetic Data Generation for Robotics 3D Scene Generation
Galbot · Zhejiang University · Peking University +3
EVIS: Real-Time Event Camera Simulation with Multimodal Supervision in NVIDIA Isaac Sim
Date pendingLinli Shi, Ruijun Zhang, Ziyun WangEvent-Based Vision Robotics Simulation
Johns Hopkins University

Feb 12, 2026, cs.ROJ/K move · Enter open · S save

ReaDy-Go: Real-to-Sim Dynamic 3D Gaussian Splatting Simulation for Environment-Specific Visual Navigation with Moving Obstacles

Seungyeon Yoo, Youngseok Jang, Dabin Kim, Youngsoo Han+2

Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea · InnoCORE AI-Transformed Aerospace Research Center, KAIST, Daejeon, Republic of Korea

Robotics Simulation Robot Navigation Synthetic Data Generation for Robotics Robot Policy Generalization Dynamic Obstacle Avoidance

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Visual navigation models often struggle in real-world dynamic environments due to limited robustness to the sim-to-real gap and the difficulty of training policies tailored to target deployment environments (e.g., households, restaurants, and factories). Although real-to-sim navigation simulation using 3D Gaussian Splatting (GS) can mitigate these challenges, prior GS-based works have considered only static scenes or non-photorealistic human obstacles built from simulator assets, despite the importance of safe navigation in dynamic environments. To address these issues, we propose ReaDy-Go, a novel real-to-sim simulation pipeline that synthesizes photorealistic dynamic scenarios in target environments by augmenting a reconstructed static GS scene with dynamic human GS obstacles, and trains navigation policies using the generated datasets. The pipeline provides three key contributions: (1) a dynamic GS simulator that integrates static scene GS with a human animation module, enabling the insertion of animatable human GS avatars and the synthesis of plausible human motions from 2D trajectories, (2) a navigation dataset generation framework that leverages the simulator along with a robot expert planner designed for dynamic GS representations and a human planner, and (3) robust navigation policies to both the sim-to-real gap and moving obstacles. The proposed simulator generates thousands of photorealistic navigation scenarios with animatable human GS avatars from arbitrary viewpoints. ReaDy-Go outperforms baselines across target environments in both simulation and real-world experiments, demonstrating improved navigation performance even after sim-to-real transfer and in the presence of moving obstacles. Moreover, zero-shot sim-to-real deployment in an unseen environment indicates its generalization potential. Project page: https://syeon-yoo.github.io/ready-go-site/.

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