High-Fidelity Simulated Data Generation for Real-World Zero-Shot Robotic
  Manipulation Learning with Gaussian Splatting
Authors: name: Haoyu Zhao, name: Cheng Zeng, name: Linghao Zhuang, name: Yaxi Zhao, name: Shengke Xue, name: Hao Wang, name: Xingyue Zhao, name: Zhongyu Li, name: Kehan Li, name: Siteng Huang, name: Mingxiu Chen, name: Xin Li, name: Deli Zhao, name: Hua Zou
Organization: DAMO Academy

The scalability of robotic learning is fundamentally bottlenecked by the
significant cost and labor of real-world data collection. While simulated data
offers a scalable alternative, it often fails to generalize to the real world
due to significant gaps in visual appearance, physical properties, and object
interactions. To address this, we propose RoboSimGS, a novel Real2Sim2Real
framework that converts multi-view real-world images into scalable,
high-fidelity, and physically interactive simulation environments for robotic
manipulation. Our approach reconstructs scenes using a hybrid representation:
3D Gaussian Splatting (3DGS) captures the photorealistic appearance of the
environment, while mesh primitives for interactive objects ensure accurate
physics simulation. Crucially, we pioneer the use of a Multi-modal Large
Language Model (MLLM) to automate the creation of physically plausible,
articulated assets. The MLLM analyzes visual data to infer not only physical
properties (e.g., density, stiffness) but also complex kinematic structures
(e.g., hinges, sliding rails) of objects. We demonstrate that policies trained
entirely on data generated by RoboSimGS achieve successful zero-shot
sim-to-real transfer across a diverse set of real-world manipulation tasks.
Furthermore, data from RoboSimGS significantly enhances the performance and
generalization capabilities of SOTA methods. Our results validate RoboSimGS as
a powerful and scalable solution for bridging the sim-to-real gap.