Genie Sim 3.0：人形机器人综合仿真平台
Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
AI总结：
Genie Sim 3.0通过高保真场景生成和开放数据集，提升机器人学习模型的泛化能力与仿真到现实的迁移效果。
摘要：
鲁棒且通用的机器人学习模型的发展严重依赖于大规模、多样化的训练数据和可靠的评估基准。在物理世界中收集数据面临高昂成本和可扩展性挑战，现有仿真基准常存在碎片化、范围狭窄或保真度不足的问题。为解决这些问题，我们引入Genie Sim 3.0，一个统一的机器人操作仿真平台。我们提出了Genie Sim Generator，一个基于大语言模型（LLM）的工具，能够从自然语言指令中构建高保真场景。其核心优势在于快速且多维的泛化能力，促进多样化环境的合成，支持可扩展的数据收集和稳健的策略评估。我们引入了首个利用LLM进行自动化评估的基准，利用LLM大规模生成评估场景，并利用视觉-语言模型（VLM）建立自动化评估流程。我们还发布了包含超过10000小时合成数据的开源数据集，覆盖超过200项任务。通过系统性实验，我们验证了开源数据集的鲁棒零样本仿真到现实迁移能力，证明在受控条件下，合成数据可以作为真实数据的有效替代，用于可扩展的策略训练。代码和数据集详情请参考：https://github.com/AgibotTech/genie_sim。
英文摘要：
The development of robust and generalizable robot learning models is critically contingent upon the availability of large-scale, diverse training data and reliable evaluation benchmarks. Collecting data in the physical world poses prohibitive costs and scalability challenges, and prevailing simulation benchmarks frequently suffer from fragmentation, narrow scope, or insufficient fidelity to enable effective sim-to-real transfer. To address these challenges, we introduce Genie Sim 3.0, a unified simulation platform for robotic manipulation. We present Genie Sim Generator, a large language model (LLM)-powered tool that constructs high-fidelity scenes from natural language instructions. Its principal strength resides in rapid and multi-dimensional generalization, facilitating the synthesis of diverse environments to support scalable data collection and robust policy evaluation. We introduce the first benchmark that pioneers the application of LLM for automated evaluation. It leverages LLM to mass-generate evaluation scenarios and employs Vision-Language Model (VLM) to establish an automated assessment pipeline. We also release an open-source dataset comprising more than 10,000 hours of synthetic data across over 200 tasks. Through systematic experimentation, we validate the robust zero-shot sim-to-real transfer capability of our open-source dataset, demonstrating that synthetic data can server as an effective substitute for real-world data under controlled conditions for scalable policy training. For code and dataset details, please refer to: https://github.com/AgibotTech/genie_sim.
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。
