Realizing scaling laws in embodied AI has become a focus. However, previous work has been scattered across diverse simulation platforms, with assets and models lacking unified interfaces, which has led to inefficiencies in research. To address this, we introduce InfiniteWorld, a unified and scalable simulator for general vision-language robot interaction built on Nvidia Isaac Sim. InfiniteWorld encompasses a comprehensive set of physics asset construction methods and generalized free robot interaction benchmarks. Specifically, we first built a unified and scalable simulation framework for embodied learning that integrates a series of improvements in generation-driven 3D asset construction, Real2Sim, automated annotation framework, and unified 3D asset processing. This framework provides a unified and scalable platform for robot interaction and learning. In addition, to simulate realistic robot interaction, we build four new general benchmarks, including scene graph collaborative exploration and open-world social mobile manipulation. The former is often overlooked as an important task for robots to explore the environment and build scene knowledge, while the latter simulates robot interaction tasks with different levels of knowledge agents based on the former. They can more comprehensively evaluate the embodied agent's capabilities in environmental understanding, task planning and execution, and intelligent interaction. We hope that this work can provide the community with a systematic asset interface, alleviate the dilemma of the lack of high-quality assets, and provide a more comprehensive evaluation of robot interactions.

翻译：实现具身智能的规模化定律已成为研究焦点。然而，先前工作分散于多种仿真平台，其资产与模型缺乏统一接口，导致研究效率低下。为此，我们提出InfiniteWorld——一个基于Nvidia Isaac Sim构建的、面向通用视觉-语言机器人交互的统一可扩展仿真器。InfiniteWorld包含一套完整的物理资产构建方法与通用自由机器人交互基准。具体而言，我们首先构建了用于具身学习的统一可扩展仿真框架，该框架整合了生成驱动三维资产构建、Real2Sim、自动化标注框架与统一三维资产处理等一系列改进。该框架为机器人交互与学习提供了统一且可扩展的平台。此外，为模拟真实机器人交互，我们构建了四项新的通用基准，包括场景图协同探索与开放世界社交移动操控。前者作为机器人探索环境与构建场景知识的重要任务常被忽视，而后者则基于前者模拟了与不同知识层次智能体交互的机器人任务。这些基准能更全面地评估具身智能体在环境理解、任务规划执行与智能交互方面的能力。我们期望本工作能为学界提供系统化的资产接口，缓解高质量资产匮乏的困境，并为机器人交互提供更全面的评估体系。