1. Bibliographic Information

1.1. Title

InfiniteWorld: A Unified Scalable Simulation Framework for General Visual-Language Robot Interaction

1.2. Authors

The paper lists numerous authors from multiple institutions:

• Pengzhen Ren* from Peng Cheng Laboratory

• Min Li*, Xinshuai Song*, Ziwei Chen*, Weijia Liufu*, Yixuan Yang*, Hao Zheng*, Zitong Huang*, Tongsheng Ding*, Luyang Xie*, Kaidong Zhang*, Changfei Fu*, Yang Liu*, Liang Lin*, Feng Zheng*, Xiaodan Liang* from Sun Yat-sen University and Southern University of Science and Technology.

• Rongtao Xu* from MBZUAI.

  The asterisks (*) typically denote equal contribution. Their backgrounds span various fields within AI and Robotics, focusing on embodied AI, computer vision, natural language processing, and simulation.

1.3. Journal/Conference

The paper is published as a preprint on arXiv, indicated by the link https://arxiv.org/abs/2412.05789. arXiv is a widely recognized platform for sharing research in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Papers on arXiv are not peer-reviewed in the same way as traditional journals or conferences, but it serves as a crucial platform for rapid dissemination of scientific findings. The publication date (2024-12-08T02:59:04.000Z) suggests it's a very recent work.

1.4. Publication Year

2024

1.5. Abstract

The abstract introduces InfiniteWorld, a unified and scalable simulation framework designed for general visual-language robot interaction, built upon Nvidia Isaac Sim. It aims to address the current inefficiencies in embodied AI research caused by scattered simulation platforms and a lack of unified interfaces for assets and models. InfiniteWorld integrates several advancements: generation-driven 3D asset construction, Real2Sim capabilities, an automated annotation framework, and unified 3D asset processing. Furthermore, the framework proposes four new general benchmarks for realistic robot interaction, including Scene Graph Collaborative Exploration (SGCE) for environmental understanding and knowledge building, and Open-World Social Mobile Manipulation (OWSMM) for simulating agent interactions with varying knowledge levels. The authors hope this work provides a systematic asset interface, alleviates the shortage of high-quality assets, and offers a more comprehensive evaluation of robot interactions.

1.6. Original Source Link

https://arxiv.org/abs/2412.05789 (Preprint) PDF Link: https://arxiv.org/pdf/2412.05789v1.pdf

2. Executive Summary

2.1. Background & Motivation

The core problem the paper addresses is the scattered and inefficient state of embodied AI research. Specifically:

1. Lack of Unified Platforms and Interfaces: Previous work in embodied AI has been fragmented across diverse simulation platforms, with assets and models lacking unified interfaces. This leads to redundant efforts, difficulties in sharing resources, and hampers the scalability of research.

2. Scarcity of High-Quality, Diverse Assets: Creating high-quality, realistic, and varied 3D assets (scenes and objects) for simulations is labor-intensive, costly, and often done in a fragmented manner, limiting the ability to generate large-scale, diverse datasets crucial for realizing scaling laws in embodied AI.

3. Limited Realism in Robot Interaction Benchmarks: Existing embodied benchmarks predominantly focus on conventional tasks like object localization, navigation, or manipulation. While there's growing interest in social navigation, current simulators often lack the ability to simulate truly realistic, human-like social interactions, particularly in scenarios where communication is constrained or knowledge levels vary among agents. The absence of a unified platform that can support these complex interactions and provide comprehensive evaluation is a significant gap.

   This problem is important because scaling laws in embodied AI, similar to those observed in large language models, require vast amounts of diverse and high-quality data. Simulation is a promising alternative to expensive real-world data collection. However, the existing fragmentation and asset limitations hinder the progress towards general-purpose, intelligent embodied agents capable of complex interactions in open-world environments.

The paper's entry point and innovative idea is to build InfiniteWorld, a unified and scalable simulation framework based on Nvidia Isaac Sim. This framework aims to provide a systematic solution for asset generation, processing, and comprehensive benchmarks for realistic, human-like robot interactions, thereby addressing the key challenges of scalability and realism in embodied AI.

2.2. Main Contributions / Findings

The paper's primary contributions are:

• A Unified and Scalable Simulation Framework: InfiniteWorld integrates generation-driven 3D asset construction, Real2Sim capabilities, an automated annotation framework, and unified 3D asset processing onto Nvidia Isaac Sim. This framework aims to provide a systematic asset interface and facilitate the unlimited expansion of scenarios and high-quality embodied assets.

• Web-based Smart Point Cloud Automatic Annotation Framework (Annot8-3D): Development of a novel web-based annotation tool that supports distributed collaboration, AI assistance, and optional human-in-the-loop features for efficient and accurate 3D point cloud labeling, which is crucial for creating high-quality datasets for complex robot interactions.

• Systematic and Comprehensive Robot Interaction Benchmarks: Introduction of four new general benchmarks designed for evaluating embodied agents in open-world scenarios:

  • Object Loco-Navigation: Basic task for navigating to target objects based on language instructions.

  • Loco-Manipulation: Extends navigation with object manipulation actions.

  • Scene Graph Collaborative Exploration (SGCE): A novel task focusing on multi-robot collaborative environmental exploration and knowledge building through scene graph construction.

  • Open-World Social Mobile Manipulation (OWSMM): A novel benchmark simulating realistic human-like social interactions, divided into hierarchical interaction (with an "administrator" agent having global knowledge) and horizontal interaction (agents with equal knowledge exchange information).

    The key findings from their evaluations include:

• LLM-based agents (e.g., GPT-4o) show strong performance in Object Loco-Navigation and Loco-Manipulation when provided with appropriate interfaces and task decomposition capabilities.

• VLM (Vision-Language Model) zero-shot approaches currently struggle with both Loco-Navigation and Loco-Manipulation, indicating a significant gap in their ability to directly generate actions from raw observations for complex tasks.

• Multi-agent planning (e.g., Co-NavGPT) can improve Scene Graph Collaborative Exploration over single-agent or random methods, with GPT-4 performing better than GPT-4o in their specific prompt design.

• Open World Social Mobile Manipulation remains a significant challenge, particularly for VLM zero-shot methods, even with the introduction of action primitives, largely due to the coarseness of semantically built maps and discrepancies between parsed and actual object locations.

  These findings highlight the potential of unified simulation platforms and generative AI for asset creation, while also underscoring the ongoing challenges in developing truly robust and socially intelligent embodied agents.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand the InfiniteWorld paper, a novice reader should be familiar with several core concepts in robotics, artificial intelligence, and computer graphics:

• Embodied AI: This is a branch of artificial intelligence concerned with intelligent agents that learn and operate within a physical body (real or simulated) and interact with the environment. Unlike disembodied AI (e.g., chatbots), embodied agents perceive the world through sensors, act upon it through effectors, and learn from these interactions. The goal is to develop AI that can perform complex tasks in dynamic, real-world environments.

• Simulation Framework/Simulator: A software environment that mimics real-world physics, visual appearance, and interactions, allowing robots or agents to operate in a virtual world. Simulators are crucial for training and testing embodied AI without the high costs, risks, and time constraints of real-world deployment. They enable rapid experimentation, large-scale data generation, and testing of algorithms in controlled, reproducible environments.

• Nvidia Isaac Sim: A robotics simulation platform built on NVIDIA Omniverse. It provides physically accurate simulation environments, high-fidelity rendering, and tools for synthetic data generation for training AI models. Its strengths include realistic physics, asset creation, and support for various robotic platforms.

• Vision-Language Models (VLMs): AI models that can process and understand both visual information (images/videos) and linguistic information (text). They can perform tasks like image captioning, visual question answering, and grounding natural language instructions in visual scenes. Examples include GPT-4o with its visual capabilities or models like Qwen-VL2.

• Large Language Models (LLMs): AI models trained on vast amounts of text data, capable of understanding, generating, and reasoning with human language. They excel at tasks like text generation, summarization, translation, and instruction following. Examples include GPT-4o, Qwen-turbo, and Chat-GLM4-flash. In embodied AI, LLMs are often used for high-level task planning, instruction parsing, and human-robot dialogue.

• 3D Assets (Scenes and Objects): These are digital models representing environments (scenes) or individual items (objects) in a 3D simulation. Scenes define the layout, geometry, and materials of an environment (e.g., a living room, an office). Objects are individual items within a scene (e.g., a chair, a cup, a robot). The quality, diversity, and interactivity of these assets are critical for realistic simulations.

• Scene Graph: A data structure that represents the objects in a scene and their relationships. It typically includes nodes for objects (with properties like type, location, size) and edges for relationships (e.g., "cup is on table," "robot is next to door"). Scene graphs provide a structured, semantic understanding of an environment, which is valuable for robot perception, reasoning, and planning.

• Real2Sim (Reality-to-Simulation): The process of taking real-world data (e.g., photographs, 3D scans) and converting it into high-fidelity models and environments suitable for use in a simulator. This helps to bridge the reality gap – the discrepancy between simulation and the real world – by making simulated environments more realistic and representative of actual conditions.

• 3D Gaussian Splatting (3DGS): A novel real-time radiance field rendering technique used for high-quality 3D reconstruction from images. It represents a scene as a collection of 3D Gaussian distributions, allowing for fast rendering and high fidelity. Variants like PGSR (Planar-based Gaussian Splatting) aim to improve geometry reconstruction, especially for planar surfaces.

• Unified Data Format (.usd): Universal Scene Description (USD) is an open-source 3D scene description technology developed by Pixar. It provides a robust scheme for interchange and collaboration on 3D data. By unifying various assets into .usd, different simulation platforms and tools can more easily share and interact with 3D models.

3.2. Previous Works

The paper extensively references existing simulators, asset generation techniques, and interaction paradigms. Here’s a summary of key prior studies and their relevance:

• Embodied AI Simulators:

  • Habitat [58]: A prominent platform focusing on photorealistic 3D environments (often scanned real-world scenes like Matterport3D) and efficient navigation tasks. Habitat 2.0 [64] extended this to include physical interactions and manipulation. Habitat 3.0 [52] further introduced human-in-the-loop paradigms and social interaction.
  • iGibson [31, 60]: Offers object-centric simulation for everyday household tasks with realistic physics and interactive objects.
  • THOR (e.g., AI2-THOR [9], ProcTHOR [10], ManipulaTHOR [13]): A family of simulators supporting navigation, manipulation, and procedural generation of scenes. ProcTHOR-10k specifically focuses on large-scale procedural generation.
  • RLBench [25]: A benchmark for robot learning focusing on manipulation skills.
  • GRUtopia [68]: A recent platform for general robots in a city at scale, which includes social navigation and introduces NPCs (Non-Player Characters) with global environment information to assist robots.
  • Orbit [42]: A unified simulation framework for interactive robot learning environments.
  • RoboCasa [45]: Focuses on large-scale simulation of everyday tasks, using semi-automated methods for scene generation.

• Interaction in Simulator:

  • Prior work on social interaction (e.g., Habitat 3.0 [52], GRUtopia [68]) has explored human-robot collaboration or NPC-assisted tasks. Habitat 3.0 uses LLMs to simulate human behaviors, while GRUtopia employs NPCs with a "God's perspective" (global ground truth information) to provide interactive assistance. InfiniteWorld critiques this "God's perspective" as unrealistic and proposes a more human-like LLM-driven interaction paradigm based on environmental exploration and varying knowledge levels.

• Scene and Asset Handling:

  • 3D Gaussian Splatting (3DGS) [27]: A foundational technique for real-time radiance field rendering and 3D reconstruction. Variants like GauStudio [78], SuGaR [23], and PGSR [7] improve upon it, often focusing on better geometry reconstruction. The paper builds its Real2Sim pipeline on an improved PGSR.
  • Large-scale 3D Scene Generation: HOLODECK [77] is highlighted for its language-guided generation of 3D embodied AI environments using a widespread 3D asset database. InstructScene [34] also uses language-driven scene synthesis. InfiniteWorld leverages HOLODECK for its generation-driven 3D asset construction.
  • 3D Object Generation: Techniques for single image to 3D object reconstruction [65] and controllable articulation generation [37] are mentioned as crucial for enriching asset libraries.
  • Open-source 3D Assets: The paper integrates diverse existing datasets like HSSD [28], HM3D [53], Replica [63], ScanNet [8] (for scenes), and 3D Front [17], PartNet-mobility [43], Objaverse [11], ClothesNet [82] (for objects).

3.3. Technological Evolution

The field of embodied AI simulation has evolved from:

1. Abstract Symbolic Reasoning: Early simulators like VirtualHome [51] and ALFRED [61] focused on high-level task planning and symbolic interactions, abstracting away complex physics.

2. Navigation in Scanned Scenes: Platforms like Habitat [58] introduced photorealistic 3D environments derived from real-world scans, enabling more realistic navigation research.

3. Realistic Physical Interaction: Habitat 2.0 [64], ManiSkill2 [22], iGibson [31], TDW [19], and SoftGym [35] brought increasingly sophisticated physics engines and deformable object simulation, narrowing the reality gap.

4. Generative AI for Scene/Task Creation: Recent advancements leverage generative models and LLMs (e.g., RoboGen [69], MimicGen [40]) to automatically create diverse scenes and tasks, addressing the manual labor bottleneck.

5. Social Interaction: The latest trend, seen in Habitat 3.0 [52] and GRUtopia [68], focuses on more complex human-like social interactions, multi-agent collaboration, and human-in-the-loop paradigms.

   InfiniteWorld fits into this evolution by attempting to unify the best aspects of these developments, particularly focusing on scalable asset generation (leveraging generative AI and Real2Sim) and realistic social interaction benchmarks (addressing the limitations of "God's perspective" NPCs).

3.4. Differentiation Analysis

Compared to other platforms and approaches, InfiniteWorld offers several core differences and innovations:

• Unified Framework on Isaac Sim: While many simulators exist, InfiniteWorld aims for a systematic and unified design on Nvidia Isaac Sim, which is known for its high-fidelity physics and rendering. This contrasts with the "scattered across diverse simulation platforms" issue. It integrates various state-of-the-art tools for asset creation and processing into one coherent ecosystem.
• Scalable Asset Generation and Unification:
  • Generation-Driven Assets: It moves beyond manual or semi-automated asset creation by integrating language-driven 3D scene generation (via HOLODECK and custom editing) and single image to 3D object reconstruction. This promises unlimited scaling of scene and object assets, a significant advantage over platforms with fixed or labor-intensive asset libraries.
  • Improved Real2Sim Pipeline: It builds upon 3D Gaussian Splatting variants with novel depth and normal vector regularization to achieve higher quality and more robust real-to-sim scene reconstruction, especially for challenging surfaces like reflective ones.
  • Unified Asset Interface: It addresses the interoperability dilemma by converting disparate open-source assets (from HSSD, HM3D, PartNet-mobility, etc.) into a unified .usd format compatible with Isaac Sim, making them readily usable.
• Novel Automated Annotation (Annot8-3D): The introduction of a web-based, AI-assisted, multi-stage point cloud annotation framework provides critical support for generating high-quality, comprehensively labeled data, which is often a bottleneck in embodied AI. This is a unique feature compared to many existing simulators that rely on external or less sophisticated annotation methods.
• Realistic Social Interaction Benchmarks:

  • Scene Graph Collaborative Exploration (SGCE): This benchmark addresses an often-overlooked aspect: how robots build world knowledge through exploration and collaboration. It moves beyond simple navigation by emphasizing the construction of scene graphs.

  • Open-World Social Mobile Manipulation (OWSMM): This is a key differentiator. Unlike GRUtopia's NPCs with "God's perspective," OWSMM simulates more realistic human-like interaction by introducing varying knowledge levels (hierarchical interaction with an administrator) and peer-to-peer knowledge exchange (horizontal interaction), requiring agents to actively seek or share information, which better reflects real-world social dynamics.

    In essence, InfiniteWorld aims to be a comprehensive, systematic, and highly scalable platform that not only provides realistic simulation but also focuses on enabling the creation of vast, high-quality datasets and benchmarking complex, socially intelligent robot behaviors, distinguishing it from platforms that might specialize in only one or two of these aspects.

4. Methodology

The InfiniteWorld simulator is designed as a unified and scalable framework built on Nvidia Isaac Sim. Its core methodology revolves around three main pillars: scalable asset construction, an advanced annotation framework, and novel robot interaction benchmarks.

4.1. Principles

The core idea behind InfiniteWorld is to provide a comprehensive ecosystem for embodied AI research that addresses the limitations of existing platforms, primarily the fragmentation of assets and lack of realistic, scalable interaction benchmarks. The theoretical basis is that for embodied AI to achieve scaling laws (i.e., performance improving predictably with increased data and model size, similar to LLMs), a continuous supply of diverse, high-quality, and interactive simulation data is essential. This requires:

1. Massive and Diverse 3D Assets: Scenes and objects must be easily generated, varied, and physically accurate.

2. High-Quality Annotation: Data needs precise labeling to be useful for learning.

3. Realistic Interaction Paradigms: Benchmarks must reflect the complexity of real-world human-robot and multi-robot interactions.

   InfiniteWorld integrates state-of-the-art techniques from generative AI, 3D reconstruction, and annotation to meet these requirements within a unified Isaac Sim environment.

4.2. Core Methodology In-depth

The InfiniteWorld framework integrates a series of improvements and novel components to achieve its goals. Figure 4 (from the original paper) provides a high-level overview of the main features.

该图像是示意图，展示了不同场景以及与机器人交互相关的资产类型，包括室内外生成场景、各种家具、传感器与机器人模型等，强调了针对视觉-语言机器人互动的统一框架。

4.2.1. Generate-Driven 3D Asset Construction

To address the challenges of cost and diversity in building large-scale, interactive, and realistic environments, InfiniteWorld integrates multiple methods for generation-driven 3D asset construction.

4.2.1.1. Language-Driven 3D Scene Generation

InfiniteWorld leverages HOLODECK [77] as a foundation for language-driven 3D scene generation. HOLODECK uses text prompts to guide the creation of 3D environments, employing a vast 3D asset database to ensure semantic accuracy, good spatial layout, and interactivity. The authors further enhance this by implementing:

• Automated Expansion of Scene Styles: Based on HOLODECK, InfiniteWorld enables automated expansion of user-defined scene assets on Isaac Sim. This includes free replacement of 236 different textures for floors and walls. This means that a base set of scenes can be expanded 236 times in terms of style variations, easily achieving infinite expansion of the scene visual properties.

• Object Editing Operations: The framework supports various editing operations on object assets within a scene, such as similar replacement, deletion, addition, and texture replacement (e.g., color, texture, quantity).

• Scale of Generated Scenes: Using this method, the authors initially constructed 10,000 indoor scenes, including household (with 1-5 room numbers per scene) and social environments (offices, restaurants, gyms, shops). By applying the scene-style replacement, they generated a total of 2.36 million scenes ($10,000 \times 236$).

  Figure 1 illustrates some examples of this language-driven automated scene generation and editing.

  该图像是示意图，展示了使用 Isaac Sim 渲染生成场景布局的编辑示例。图中包含了不同的编辑指令，如改变风格、替换物体等，展示了在居住环境中进行物品添加和修改的具体实例。

4.2.1.2. Single Image to 3D Object Reconstruction

To further enrich the asset library with diverse interactive scenarios, InfiniteWorld integrates existing single image to 3D object asset reconstruction [65] techniques. This allows for creating 3D models of objects from just a single 2D image, significantly expanding the variety of objects available in the simulation.

4.2.1.3. Controllable Articulation Generation

The simulator also integrates controllable articulation generation [37]. This refers to the ability to generate 3D models of articulated objects (objects with moving parts, like doors, drawers, or robots) where the movement of these parts can be controlled programmatically. This is crucial for simulating realistic robot manipulation tasks involving complex objects.

4.2.2. Depth-Prior-Constrained Real2Sim

The Real2Sim pipeline in InfiniteWorld aims to reconstruct accurate and visually coherent 3D models from photographic data, addressing the challenge of reflections on smooth surfaces that can interfere with traditional Structure-from-Motion (SfM) methods. It improves upon PGSR [7], a 3D Gaussian Splatting variant.

The 3D scene reconstruction pipeline involves several sequential steps:

1. SfM (Structure-from-Motion): The process begins with colmap-glomap [50], an SfM approach that estimates the camera parameters (e.g., intrinsic and extrinsic properties of the camera for each image) and produces a sparse point cloud. A sparse point cloud is a collection of 3D points representing prominent features in the scene, whose positions are estimated from multiple 2D images.

2. Novel View Synthesis (NVS) & Meshing:

   • NVS is achieved through the improved PGSR [7]. PGSR (Planar-based Gaussian Splatting for efficient and high-fidelity surface reconstruction) is a technique that uses 3D Gaussian Splats to represent the scene, specifically optimized for planar surfaces.
   • To improve PGSR for challenging surfaces, the authors introduce two types of regularization loss based on depth and normal vector.
     • Depth Regularization: A pre-trained depth estimation model, Depth Pro [4], is used to generate depth estimates for each RGB image in the camera coordinate system. This provides additional supervision, helping the model to correctly infer the distance of points from the camera.
     • Normal Vector Regularization: The Local Plane Assumption [7] from PGSR is used to compute plane normal vectors. These vectors describe the orientation of surfaces. Providing this as extra supervision helps PGSR better model surface geometry, especially for flat or reflective areas where reflections might otherwise lead to incorrect surface reconstruction.
   • After NVS, mesh extraction is conducted using Truncated Signed Distance Function (TSDF) [46] and the Marching Cubes algorithm [39]. TSDF is a common method for representing implicit surfaces, and Marching Cubes is an algorithm for reconstructing a polygonal mesh from an implicit surface.

3. Z-Axis Alignment: To ensure the reconstructed scene has the correct vertical orientation, the Random Sample Consensus (RANSAC) [14] algorithm is employed. RANSAC is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, by robustly detecting the dominant plane (e.g., the ground plane) and aligning the entire scene's vertical axis (Z-axis) accordingly.

4. Denoising: A connectivity-cluster approach is used to filter extraneous points (noise) from high spatial areas, setting a threshold to remove points that are not part of significant clusters. This reduces model complexity and improves visual clarity.

5. Hole-Filling: Small gaps or holes in the reconstructed mesh are closed using PyMeshFix [3]. This preserves the structural continuity and integrity of the model.

6. Recoloring: During the hole-filling process, some color information might be lost. To restore this, colors from the original images are mapped back to the mesh vertices using KDTree [16]. A KDTree (k-dimensional tree) is a data structure used for organizing points in a k-dimensional space, facilitating efficient nearest-neighbor searches, which is useful here for finding corresponding color information.

7. Simplification: Finally, PyMeshLab [44] is used to reduce vertex density, optimizing the model size while retaining essential geometry.

   Figure 2 demonstrates the improved reconstruction effects of their method compared to other approaches like GauStudio, SuGaR, and PGSR in handling challenging surfaces. Figure 6 shows the impact of the post-processing steps.

   该图像是一个示意图，展示了不同方法（GauStudio、SuGaR、PGSR、Ours 和 GT）在模拟环境中对屏幕、门、墙、橱柜和桌子的处理效果对比。通过可视化的方式，体现了各方法在视觉表现上的差异。

   该图像是对比重构结果的示意图，左侧为未经过后处理，右侧为经过后处理的结果。该图展示了后处理方法在修复场景中的孔洞和去除浮动网格的有效性，特别是在柜子、沙发和桌子上方的浮动物体，以及周围的缝隙。

4.2.3. Annot8-3D: Automatic Annotation Framework

Annot8-3D is a novel web-based smart point cloud automatic annotation framework designed for efficient and accurate 3D point cloud labeling. It combines AI-assisted automation with human-in-the-loop refinement. The framework implements a multi-stage annotation pipeline:

1. Initial Coarse Segmentation:
   • The pipeline starts with automated coarse-grained segmentation using Point Transformer V3 [72]. This model provides initial object proposals across the entire point cloud, generating a preliminary segmentation.
2. Interactive Refinement:
   • Human reviewers can examine and refine these coarse segmentation results. This is done through positive and negative prompts (e.g., clicking on a point and saying "this is part of the object" or "this is not").
   • This stage integrates SAM2Point [24] to process these prompts and generate refined segmentations. SAM2Point is a model designed for interactive 3D segmentation using prompts.
   • This allows for iterative refinement loops until satisfactory results are achieved.
3. Manual Fine-tuning (Optional):

   • For cases where automated or interactive refinement is insufficient, the framework provides manual segmentation tools for precise adjustments.

     Annot8-3D supports distributed collaboration, AI assistance, and has optional human-in-the-loop features, streamlining the creation of scene assets and the formulation of interactive tasks. It also supports common 3D point cloud formats and a comprehensive attribute schema for physical and semantic properties relevant to robotics (e.g., unique identifiers, collision characteristics, friction coefficients, manipulability flags, instance segmentation, position, room assignments, orientation, semantic labels, appearance).

Figure 3 illustrates the Annot8-3D framework pipeline.

该图像是示意图，展示了Annot8-3D框架的处理流程。包括三个阶段：初步粗略分割、交互式细化和手动微调（可选）。图中左侧为初步分割结果，右侧为经过细化的植物模型。

4.2.4. Unified 3D Asset

To overcome the asset interoperability issue (where existing popular 3D assets use different data formats and are tied to different simulation platforms), InfiniteWorld provides a unified interface for assets from various open-source sources, all integrated onto the Isaac Sim platform.

• Standardization to .usd: All assets are unified into the .usd (Universal Scene Description) format, enabling seamless calling and interaction within Isaac Sim. Conversion scripts are provided to facilitate this process and ensure physical simulation compatibility.

• Integrated Scene-level Assets:

  • HSSD [28] (Habitat Synthetic Scenes Dataset)
  • HM3D [53] (Habitat-Matterport 3D Dataset)
  • Replica [63] (A digital replica of indoor spaces)
  • ScanNet [8] (Richly-annotated 3D reconstructions of indoor scenes)

• Integrated Object-level Assets:

  • 3D Front [17] (3D furnished rooms with layouts and semantics)
  • PartNet-mobility [43] (Large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding, including articulated objects)
  • Objaverse [11] (A universe of 3D objects, specifically mentioned as Objaverse (Holodeck))
  • ClothesNet [82] (Information-rich 3D garment model repository)

• Special Object Simulation: The platform also implements simulation for special objects like soft bodies (e.g., deformable clothes from ClothesNet) and transparent objects, which are critical for realistic physics simulations and complex manipulation tasks.

  The comprehensive unification of these diverse assets into Isaac Sim provides a rich and massive resource for embodied learning, accelerating the adoption of scaling laws.

5. Experimental Setup

The experiments in InfiniteWorld are designed to evaluate embodied agents across four benchmarks, focusing on navigation, manipulation, exploration, and social interaction.

5.1. Datasets

The paper describes using InfiniteWorld as a simulation environment that incorporates existing datasets as assets, rather than using traditional datasets for training a specific model being proposed.

• Scene Assets: The scenes used in the benchmarks are drawn from the 10,000 indoor scenes constructed by InfiniteWorld's language-driven 3D asset construction method, which includes household and social environments. These scenes are further expanded to 2.36 million variations. They are also unified from datasets like HSSD [28], HM3D [53], Replica [63], and ScanNet [8].
• Object Assets: The objects within these scenes come from integrated open-source libraries like 3D Front [17], PartNet-mobility [43], Objaverse [11], and ClothesNet [82]. These cover categories such as fruits, beverages, dolls, appliances, furniture, and articulated objects.
• Task Generation: Task instructions for the benchmarks are generated using GPT-4o [49] in combination with the scene semantics from the HSSD [28] dataset. This ensures diverse and realistic natural language instructions for the agents.

5.2. Evaluation Metrics

The paper employs a set of standard and novel metrics to evaluate agent performance across its benchmarks.

5.2.1. Object Loco-Navigation Metrics

These metrics are common in navigation tasks.

• Success Rate (SR):

  • Conceptual Definition: The proportion of tasks where the agent successfully reaches the target object. For this benchmark, success is defined as the target object appearing in the agent's field of view, with the distance between the robot and the target object being less than 2 meters, and the object being within 60 degrees of the robot's horizontal field of view.
  • Mathematical Formula: $ SR = \frac{\text{Number of successful episodes}}{\text{Total number of episodes}} $
  • Symbol Explanation:
    • Number of successful episodes: The count of task instances where the agent met the success criteria.
    • Total number of episodes: The total count of task instances attempted.

• Success weighted by Path Length (SPL):

  • Conceptual Definition: A metric that penalizes longer-than-optimal paths for successful navigation. It measures efficiency in addition to success, valuing shorter paths more highly.
  • Mathematical Formula: $ SPL = \frac{1}{N} \sum_{i=1}^{N} S_i \frac{L_i}{\max(P_i, L_i)} $
  • Symbol Explanation:
    • $N$: Total number of episodes.
    • $S_i$: Binary indicator for success in episode $i$ (1 if successful, 0 otherwise).
    • $L_i$: Length of the optimal (shortest) path for episode $i$.
    • $P_i$: Length of the path taken by the agent in episode $i$.
    • $\max(P_i, L_i)$: Ensures the ratio is always $\leq 1$ if $S_i=1$, and prevents division by zero if $P_i$ could be zero.

• Navigation Error (NE):

  • Conceptual Definition: The distance between the agent's final position and the target object's position at the end of the navigation attempt. This measures how close the agent got to the target if it didn't succeed or how precisely it reached the target if it did.
  • Mathematical Formula: The paper does not provide an explicit formula for NE, but conceptually it is the Euclidean distance: $ NE = ||\mathbf{p}{final} - \mathbf{p}{target}||_2 $
  • Symbol Explanation:
    • $\mathbf{p}_{final}$: The 3D coordinates of the agent's final position.
    • $\mathbf{p}_{target}$: The 3D coordinates of the target object.
    • $||\cdot||_2$: The Euclidean norm (distance).

5.2.2. Loco-Manipulation Metrics

Similar to Object Loco-Navigation, but these metrics evaluate the entire process of navigation and manipulation.

• SR (Success Rate): Defined similarly, but success requires both navigation to the object and successful manipulation (e.g., picking it up and placing it in the target location).
• SPL (Success weighted by Path Length): Defined similarly, but the path length might encompass the entire trajectory, including navigation to pick up and then to place the object.
• NE (Navigation Error): Defined similarly, focusing on the final placement location after manipulation.

5.2.3. Scene Graph Collaborative Exploration Metrics

These metrics specifically evaluate the agent's ability to explore an environment and build knowledge collaboratively.

• Semantic Exploration Rate (SER):

  • Conceptual Definition: The ratio of the number of unique object instances discovered by the robot(s) within a maximum exploration step limit (200 steps) to the actual total number of unique object instances present in the scene. It measures the completeness of the exploration in terms of semantic objects.
  • Mathematical Formula: $ SER = \frac{\text{Number of unique object instances discovered}}{\text{Total number of unique object instances in the scene}} $
  • Symbol Explanation:
    • Number of unique object instances discovered: Count of distinct object types correctly identified and mapped by the agent(s).
    • Total number of unique object instances in the scene: Ground truth count of distinct object types present in the environment.

• Minimum Root Mean Square Error (MRMSE):

  • Conceptual Definition: Measures the accuracy of the located objects. It calculates the root mean square error between the estimated centers of discovered objects and their corresponding actual ground truth centers. Minimum suggests a matching strategy where each discovered object is matched to its closest ground truth counterpart.
  • Mathematical Formula: The paper does not provide an explicit formula for MRMSE, but the general RMSE is: $ RMSE = \sqrt{\frac{1}{N_{discovered}} \sum_{i=1}^{N_{discovered}} ||\mathbf{c}{discovered,i} - \mathbf{c}{groundtruth,i}||_2^2} $ Where for MRMSE, each $\mathbf{c}_{discovered,i}$ is matched to its closest $\mathbf{c}_{groundtruth,j}$ (if within a threshold).
  • Symbol Explanation:
    • $N_{discovered}$: The number of object instances discovered by the robot.
    • $\mathbf{c}_{discovered,i}$: The estimated 3D center coordinates of the $i$-th discovered object.
    • $\mathbf{c}_{groundtruth,i}$: The actual 3D center coordinates of the corresponding $i$-th ground truth object.
    • $||\cdot||_2$: The Euclidean norm (distance).

5.2.4. Open World Social Mobile Manipulation Metrics

These tasks use SR and SPL from Loco-Manipulation and add metrics for action path analysis.

• SR (Success Rate): As defined for Loco-Manipulation.
• SPL (Success weighted by Path Length): As defined for Loco-Manipulation.
• Minimum Action Path (MPL):
  • Conceptual Definition: Represents the shortest possible sequence of actions required to complete the task. This often reflects the efficiency of the planning strategy.
  • Mathematical Formula: No explicit formula given, but it represents the optimal number of action steps.
• Longest Action Path (LPL):
  • Conceptual Definition: Represents the longest sequence of actions observed during the task, potentially indicating inefficient planning or exploration.
  • Mathematical Formula: No explicit formula given, but it represents the maximum number of action steps.

5.3. Baselines

The paper compares InfiniteWorld's agents against several baselines, categorized by the type of intelligence used:

• LLM-Based Instruction Following:

  • Description: This baseline uses a Large Language Model (LLM) combined with prompt engineering to decompose natural language instructions into a sequence of executable action interfaces for the embodied agent. The LLM guides the agent step-by-step to complete the task.
  • Models evaluated: GPT-4o [49], Qwen-turbo, and Chat-GLM4-flash.

• VLM Zero-Shot:

  • Description: In this setup, a Vision-Language Model (VLM) directly receives global scene information and current observations. Using prompt engineering, the VLM is asked to output the next action the agent should execute without prior fine-tuning on the specific task. This tests the VLM's inherent ability to ground language in vision and generate actions.
  • Models evaluated: GPT-4o [49] (for its visual capabilities), Qwen-VL2, and GLM-4V.

• Single Semantic Map:

  • Description: This baseline uses a single agent that builds a 2D semantic map of the environment (identifying objects and their categories). For navigation, it employs the method proposed in Goal-Oriented Semantic Exploration [5]. Path planning combines the Frontier-Based Exploration (FBE) [74] algorithm as a global planner and the Fast Marching Method (FMM) [59] for local planning.

• Random:

  • Description: This baseline represents a non-intelligent approach where actions are randomly sampled from the robot's action space. Alternatively, target points are randomly sampled in the planning space, and existing planning algorithms are used to try and reach them. This serves as a lower bound for performance.

• LLM-Based Planning (Co-NavGPT [80]):

  • Description: Specifically for multi-agent systems, this baseline uses an LLM as a planner. The merged observation map from multiple agents is converted into a textual description, which the LLM then processes to perform goal planning for the agents.
  • Models evaluated: GPT-4 and GPT-4o. (The original Co-NavGPT was based on GPT-4-turbo, so they tested GPT-4 for comparison).

• LLM-Planner [62]:

  • Description: A few-shot grounded planning model that uses LLMs to directly generate plans, rather than ranking pre-defined skills. It can dynamically adjust its plans based on current observations, requiring less prior environmental knowledge and fewer LLM calls.

Robot Setup:

• All experiments use the Stretch robot as the execution agent. The Stretch robot is a mobile manipulator with an omnidirectional base (allowing movement in any direction) and a 7-degree-of-freedom (DOF) manipulator arm, enabling it to perform complex mobile manipulation tasks.

Simulation Assistance: The paper also describes various interfaces provided by InfiniteWorld to assist with task completion and enable diverse experimental settings:

• Occupy Map: A 2D grid map representing the scene, divided into "free," "obstacle," and "unknown" areas, used for robot navigation.
• Path Follower: Utilizes the D* Lite algorithm (based on the occupancy map) for point-to-point path planning, avoiding obstacles. It can find the nearest non-colliding point if the target is in an "obstacle" area.
• Physical Manipulation: Provides joint-based robot arm control (direct target joint angles) or inverse control (specifying end-effector pose via inverse kinematics).
• Adhesion: An interface that allows the end-effector to directly attach an object within range, eliminating the need for complex grasping poses and trajectories, simplifying manipulation tasks.

6. Results & Analysis

6.1. Core Results Analysis

The experiments evaluate different models and baselines across four benchmarks: Object Loco-Navigation, Loco-Manipulation, Scene Graph Collaborative Exploration, and Open World Social Mobile Manipulation.

6.1.1. Object Loco-Navigation

For the fundamental Object Loco-Navigation task, LLM-Based Instruction Following models, particularly GPT-4o, showed superior performance. The following are the results from Table 2 of the original paper:

GPT-4o achieved an impressive SR (Success Rate) of 90.82% and an SPL (Success weighted by Path Length) of 90.82%, with a low NE (Navigation Error) of 1.00. This indicates its strong ability to understand instructions and effectively navigate to target objects when given access to navigation interfaces. The primary failure cases involved obstacles blocking the view or the agent failing to reach the required 60-degree horizontal view of the object.

Comparing other LLMs, Qwen-turbo showed more stable actions but lower accuracy in precisely locating objects (SR 69.94%), while Chat-GLM4-flash had lower stability but relatively higher action accuracy (SR 66.41%).

In stark contrast, VLM Zero-Shot models performed very poorly, with GPT-4o (VLM) achieving only 0.06% SR, and Qwen-VL2 and GLM-4V achieving 0.00% SR. Their high NE values (15.23 to 26.53) further highlight their inability to achieve the goal using direct observation and action generation in a zero-shot setting. This suggests that current VLMs struggle to translate visual understanding directly into effective navigation actions without task-specific fine-tuning or more structured guidance.

6.1.2. Loco-Manipulation

The Loco-Manipulation tasks are more complex, requiring not only navigation but also precise judgment and execution of manipulation actions. The following are the results from Table 3 of the original paper:

The trends from Loco-Navigation are amplified here. GPT-4o (LLM) still maintained the highest performance with SR of 77.28%, demonstrating its robustness across multi-stage tasks. Chat-GLM4-flash significantly outperformed Qwen-turbo in terms of SR (50.63% vs 42.64%), likely due to its higher action accuracy despite lower stability, which is critical for manipulation.

VLM Zero-Shot models again performed extremely poorly, with SR values near 0.00%. This highlights that VLMs not only struggle with basic navigation in a zero-shot context but face even greater challenges in loco-manipulation where precise object grasping and placement are required. The difficulty in determining grasp boundaries from visual input alone poses a significant hurdle for these models.

6.1.3. Scene Graph Collaborative Exploration

This benchmark evaluates how efficiently and accurately agents can explore and build scene graphs collaboratively. The following are the results from Table 4 of the original paper:

Here, Co-NavGPT with GPT-4 (an LLM-based planner for multi-agent systems) achieved the highest SER (Semantic Exploration Rate) of 0.3209, indicating it discovered a higher proportion of unique object instances. This suggests that LLM-based planning can effectively coordinate agents for exploration, especially when leveraging textual descriptions of merged observation maps. Interestingly, GPT-4 outperformed GPT-4o for Co-NavGPT, which the authors attribute to the specific prompt design used.

The Random baseline had a surprisingly high SER of 0.3030 compared to Single SemMap (0.2581), but its MRMSE (Minimum Root Mean Square Error) was much higher (7.7388 vs 5.7849), implying that while random exploration might cover more ground, it's less accurate in locating objects. Co-NavGPT with GPT-4 achieved a good balance with decent MRMSE (6.1336) alongside the highest SER.

6.1.4. Open World Social Mobile Manipulation

This benchmark explores complex social interaction scenarios. The evaluations for this benchmark are particularly challenging for current models. The following are the results from Table 5 of the original paper:

The results for all tested methods in Open World Social Mobile Manipulation show an SR and SPL of 0.00. This indicates that none of the VLM-based approaches (even with additional action primitives like $<walk>$ and $<pick>$) were able to successfully complete the mobile manipulation tasks in these social interaction settings.

The authors identify a key reason for this failure: the coarseness of semantically built maps derived from Benchmark 3 (Scene Graph Collaborative Exploration). The object instances required for the tasks often either did not appear in these coarse maps or had significant discrepancies between their parsed and actual locations. This highlights a critical limitation: even with sophisticated VLMs, the quality and accuracy of the underlying environmental knowledge representation (the scene graph/map) are paramount for complex interaction tasks. The high MPL and LPL values (minimum and longest action path, respectively) also suggest that the models are generating action sequences, but these are not leading to successful task completion, likely due to fundamental issues in perception and planning based on inaccurate environmental understanding.

6.2. Data Presentation (Tables)

The following tables are transcribed directly from the original paper.

The following are the results from Table 2 of the original paper:

The following are the results from Table 3 of the original paper:

The following are the results from Table 4 of the original paper:

The following are the results from Table 5 of the original paper:

6.3. Ablation Studies / Parameter Analysis

The paper does not present explicit ablation studies on the InfiniteWorld framework itself (e.g., removing a component of the asset generation pipeline to see its effect). Instead, it evaluates the performance of different LLM and VLM models (and their variations, like VLM Explore vs VLM Explore+Act Prim) within the InfiniteWorld benchmarks. This can be viewed as an analysis of how different intelligent agents perform given the capabilities of the InfiniteWorld simulation and asset base.

• LLM vs. VLM Performance: The most prominent "ablation" or comparative analysis is the stark difference between LLM-Based Instruction Following and VLM Zero-Shot. This highlights that LLMs, when provided with symbolic interfaces, are much more effective at high-level planning and instruction following than VLMs are at directly translating visual observations into actionable steps in a zero-shot manner. This suggests that symbolic reasoning and action decomposition capabilities of LLMs are currently superior for complex embodied tasks compared to end-to-end VLM approaches, especially without fine-tuning.

• Impact of Action Primitives: For Open World Social Mobile Manipulation, the introduction of action primitives (e.g., $<walk>$, $<pick>$) for VLM Explore+Act Prim did not improve the SR or SPL from 0.00. This suggests that the issue isn't just about the VLM's ability to generate low-level actions, but a more fundamental problem in its environmental understanding or task planning when relying on coarse semantic maps.

• LLM Model Choice (Co-NavGPT): The comparison between GPT-4 and GPT-4o for Co-NavGPT reveals that GPT-4 achieved a higher SER. This indicates that the specific LLM model, and crucially, its interaction with the prompt design for a given task, can significantly impact performance in multi-agent planning.

  Overall, these comparisons provide insights into the current capabilities and limitations of different embodied AI approaches within a complex simulation environment like InfiniteWorld, highlighting areas for future research in VLM grounding and robust map representation.

7. Conclusion & Reflections

7.1. Conclusion Summary

This paper introduces InfiniteWorld, a novel, unified, and scalable simulation framework for general visual-language robot interaction built upon Nvidia Isaac Sim. The core contribution lies in addressing the fragmented nature of embodied AI research by providing a systematic solution for asset generation and comprehensive interaction benchmarks. InfiniteWorld achieves scalability through generation-driven 3D asset construction, an improved Depth-Prior-Constrained Real2Sim pipeline, and the Annot8-3D automatic annotation framework, all integrated with a unified 3D asset interface that standardizes diverse open-source content. Furthermore, it introduces four new benchmarks—Object Loco-Navigation, Loco-Manipulation, Scene Graph Collaborative Exploration, and Open-World Social Mobile Manipulation—to enable a more comprehensive evaluation of embodied agents' capabilities in environmental understanding, task planning, execution, and realistic social interaction. The experimental results demonstrate the strong performance of LLM-based agents in navigation and manipulation tasks when provided with appropriate interfaces, while also highlighting the significant challenges still faced by VLM zero-shot methods in complex, open-world scenarios.

7.2. Limitations & Future Work

The authors implicitly highlight several limitations through their experimental results, which also suggest avenues for future work:

• VLM Limitations in Zero-Shot Embodied Tasks: The very low success rates of VLM Zero-Shot models in Loco-Navigation, Loco-Manipulation, and especially Open World Social Mobile Manipulation tasks are a significant limitation. This indicates that current VLMs struggle to directly translate raw visual observations and language instructions into effective low-level actions for complex embodied tasks without extensive fine-tuning or more structured intermediate representations.

• Coarseness of Semantic Maps for Complex Interaction: The complete failure in Open World Social Mobile Manipulation is attributed to the coarseness of semantically built maps from the Scene Graph Collaborative Exploration benchmark. This points to a limitation in the current scene graph construction and its utility for highly precise manipulation tasks, where exact object locations are critical. Future work needs to focus on building more accurate and fine-grained environmental representations that can support the demands of real-world interactions.

• Realistic Social Interaction Complexity: While InfiniteWorld proposes novel social interaction benchmarks (Hierarchical and Horizontal), the current inability of agents to succeed in these tasks (even with action primitives) shows the immense complexity. This suggests a need for more advanced LLM-driven planning that can better integrate social cues, knowledge exchange mechanisms, and uncertainty reasoning into real-time decision-making for multi-agent settings.

  The work itself lays the foundation for future research by providing a robust platform. Future work could involve:

• Developing VLM architectures or training paradigms that can bridge the gap between high-level visual-language understanding and low-level action generation for embodied agents, moving beyond zero-shot to few-shot or fine-tuned solutions.

• Improving scene graph generation and object localization accuracy to provide richer and more reliable environmental knowledge for complex manipulation tasks.

• Exploring more sophisticated multi-agent coordination strategies and LLM-driven social reasoning within the OWSMM benchmark, to enable successful knowledge sharing and collaborative task completion.

• Expanding the Real2Sim capabilities to further reduce the reality gap for a wider range of materials and environmental conditions.

7.3. Personal Insights & Critique

InfiniteWorld represents a highly ambitious and much-needed effort to consolidate and advance embodied AI research.

• Value of Unification: The most significant contribution is the creation of a unified and scalable framework on Isaac Sim. The current fragmentation of simulators and asset libraries is a major bottleneck, and InfiniteWorld's systematic integration of asset generation, Real2Sim, annotation, and diverse benchmarks is a crucial step towards fostering more efficient and reproducible research. The .usd unification is particularly valuable for interoperability.

• Power of Generative AI for Assets: Leveraging language-driven scene generation (via HOLODECK) and single image to 3D object reconstruction offers a path to truly unlimited asset scaling. This is critical for achieving scaling laws in embodied AI, as manual asset creation cannot keep pace with the data demands of large models. The 2.36 million scene variations are a testament to this potential.

• Importance of Annot8-3D: The Annot8-3D framework is a vital component. High-quality, granular annotations are often the unsung hero of successful AI systems. A web-based, AI-assisted, human-in-the-loop annotation tool directly addresses a major pain point for researchers, significantly reducing the cost and effort of data labeling.

• Critique on VLM Performance & Map Representation: The stark failure of VLM Zero-Shot models in complex manipulation and social interaction tasks is a critical insight. It highlights that while VLMs have impressive general knowledge, they still struggle with the grounding problem—translating abstract understanding into precise physical interactions and sequential planning in a dynamic 3D environment. The identified issue with coarse semantic maps for OWSMM underscores that the quality of intermediate representations (like scene graphs) is as important as the intelligence of the agent itself. A VLM can only be as good as the information it is given about the environment. This suggests that more research is needed not just on larger VLMs, but also on robust, fine-grained, and actionable environmental perception systems.

• Practicality of Social Benchmarks: The proposed SGCE and OWSMM benchmarks are innovative and move towards more realistic human-robot interaction. The distinction between hierarchical and horizontal interaction is well-thought-out, reflecting real-world scenarios where knowledge distribution varies. However, the 0% success rate in OWSMM indicates that these tasks are currently beyond the capabilities of existing models, making them excellent, challenging targets for future research.

  Overall, InfiniteWorld is a foundational work that could significantly accelerate embodied AI research by providing a powerful, unified platform and pushing the boundaries of what is considered a "realistic" simulation benchmark. Its strengths lie in its comprehensive approach to asset management and its rigorous evaluation of complex interaction paradigms.