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ReaDy-Go: 面向动态障碍物环境特定视觉导航的实到仿动态3D高斯泼溅仿真

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

Department of Aerospace Engineering, Seoul National University（首尔国立大学航空航天工程系）；InnoCORE AI-Transformed Aerospace Research Center, KAIST（韩国科学技术院（KAIST）人工智能赋能航空航天研究中心）

Seungyeon Yoo, Youngseok Jang, Dabin Kim, Youngsoo Han, Seungwoo Jung, H. Jin Kim

备注：
Accepted by IEEE Robotics and Automation Letters (RA-L). Project page: https://syeon-yoo.github.io/ready-go-site/
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AI总结：

提出ReaDy-Go仿真管线，通过将静态3D高斯场景与动态人体高斯障碍物结合生成逼真动态场景，并训练导航策略，有效缩小仿真到现实的差距并处理移动障碍物。

摘要：

视觉导航模型在真实动态环境中常常因对仿真到现实差距的鲁棒性有限以及难以训练针对目标部署环境（如家庭、餐厅和工厂）定制的策略而面临挑战。尽管使用3D高斯泼溅（GS）的实到仿导航仿真可以缓解这些问题，但先前基于GS的工作仅考虑了静态场景或由仿真器资源构建的非逼真人体障碍物，而忽略了动态环境中安全导航的重要性。为了解决这些问题，我们提出了ReaDy-Go，一种新颖的实到仿仿真管线，通过将重建的静态GS场景与动态人体GS障碍物相结合，在目标环境中合成逼真的动态场景，并使用生成的数据集训练导航策略。该管线提供了三个关键贡献：（1）一个动态GS仿真器，将静态场景GS与人体动画模块集成，能够插入可动画化的人体GS化身，并从2D轨迹合成合理的人体运动；（2）一个导航数据集生成框架，利用该仿真器以及为动态GS表示设计的机器人专家规划器和人体规划器；（3）对仿真到现实差距和移动障碍物都具有鲁棒性的导航策略。所提出的仿真器从任意视角生成数千个带有可动画化人体GS化身的逼真导航场景。ReaDy-Go在仿真和真实世界实验中均优于目标环境中的基线，即使在仿真到现实迁移后以及存在移动障碍物的情况下也表现出改进的导航性能。此外，在未见环境中的零样本仿真到现实部署表明了其泛化潜力。项目页面：此https URL。

英文摘要：

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/.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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