TongSIM: A General Platform for Simulating Intelligent Machines

2025-12-23

AI Summary

Existing embodied AI simulation platforms are highly task-specific and exhibit poor generalization, hindering unified investigation spanning low-level navigation to high-level social interaction and human-AI collaboration. To address this, we propose the first high-fidelity, general-purpose embodied AI simulation platform, encompassing over 100 multi-room indoor scenes and open outdoor urban environments. Our approach introduces three core innovations: (1) task-adaptive fidelity control, (2) dynamic environment evolution, and (3) a cross-level unified evaluation framework—integrated with multimodal perception modeling, physics-based interaction, programmable scene generation, heterogeneous agent co-simulation, and a multidimensional capability benchmark. Experiments demonstrate significant improvements in training efficiency and generalization across key competencies—including spatial reasoning, social cognition, and human-AI collaboration—thereby advancing embodied AI research from task-specific paradigms toward general-purpose foundations.

Technology Category

• Intelligent Robots: Embodied AI
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

As artificial intelligence (AI) rapidly advances, especially in multimodal large language models (MLLMs), research focus is shifting from single-modality text processing to the more complex domains of multimodal and embodied AI. Embodied intelligence focuses on training agents within realistic simulated environments, leveraging physical interaction and action feedback rather than conventionally labeled datasets. Yet, most existing simulation platforms remain narrowly designed, each tailored to specific tasks. A versatile, general-purpose training environment that can support everything from low-level embodied navigation to high-level composite activities, such as multi-agent social simulation and human-AI collaboration, remains largely unavailable. To bridge this gap, we introduce TongSIM, a high-fidelity, general-purpose platform for training and evaluating embodied agents. TongSIM offers practical advantages by providing over 100 diverse, multi-room indoor scenarios as well as an open-ended, interaction-rich outdoor town simulation, ensuring broad applicability across research needs. Its comprehensive evaluation framework and benchmarks enable precise assessment of agent capabilities, such as perception, cognition, decision-making, human-robot cooperation, and spatial and social reasoning. With features like customized scenes, task-adaptive fidelity, diverse agent types, and dynamic environmental simulation, TongSIM delivers flexibility and scalability for researchers, serving as a unified platform that accelerates training, evaluation, and advancement toward general embodied intelligence.

Problem

• Develops a versatile platform for training embodied AI agents
• Addresses the lack of general-purpose simulation environments for multimodal AI
• Enables evaluation of agent capabilities like perception and social reasoning

Innovation

• General-purpose platform for embodied agent training
• High-fidelity indoor and outdoor simulation environments
• Comprehensive evaluation framework with diverse benchmarks