GSWorld：用于机器人操作的闭环逼真仿真套件
GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation
AI总结：
GSWorld结合3D高斯泼溅与物理引擎，提出GSDF资产格式，构建含3种机器人和40+物体的数据库，实现零样本sim2real策略学习、DAgger数据收集、可重复基准测试等应用，推动机器人操作闭环开发。
摘要：
本文提出了GSWorld，一个用于机器人操作的鲁棒、逼真的仿真器，它将3D高斯泼溅与物理引擎相结合。我们的框架倡导“闭环”开发操作策略，即可重复地评估从真实机器人数据中学习的策略，并在不使用真实机器人的情况下进行sim2real策略训练。为了实现对多样化场景的逼真渲染，我们提出了一种新的资产格式，称为GSDF（高斯场景描述文件），它将高斯-网格表示与机器人URDF及其他物体融合在一起。通过简化的重建流程，我们构建了一个包含3种机器人形态（用于单臂和双臂操作）以及40多个物体的GSDF数据库。将GSDF与物理引擎结合，我们展示了几个直接有趣的应用：（1）利用逼真渲染学习零样本sim2real像素到动作操作策略，（2）自动收集高质量DAgger数据以适应部署环境，（3）在仿真中对真实机器人操作策略进行可重复基准测试，（4）通过虚拟遥操作收集仿真数据，以及（5）零样本sim2real视觉强化学习。网站：https://3dgsworld.github.io/。
英文摘要：
This paper presents GSWorld, a robust, photo-realistic simulator for robotics manipulation that combines 3D Gaussian Splatting with physics engines. Our framework advocates "closing the loop" of developing manipulation policies with reproducible evaluation of policies learned from real-robot data and sim2real policy training without using real robots. To enable photo-realistic rendering of diverse scenes, we propose a new asset format, which we term GSDF (Gaussian Scene Description File), that infuses Gaussian-on-Mesh representation with robot URDF and other objects. With a streamlined reconstruction pipeline, we curate a database of GSDF that contains 3 robot embodiments for single-arm and bimanual manipulation, as well as more than 40 objects. Combining GSDF with physics engines, we demonstrate several immediate interesting applications: (1) learning zero-shot sim2real pixel-to-action manipulation policy with photo-realistic rendering, (2) automated high-quality DAgger data collection for adapting policies to deployment environments, (3) reproducible benchmarking of real-robot manipulation policies in simulation, (4) simulation data collection by virtual teleoperation, and (5) zero-shot sim2real visual reinforcement learning. Website: https://3dgsworld.github.io/.
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。
