用离散事件模拟构建可扩展的动态场景图，支持部分观测下的多智能体环境建模。
FOGMACHINE -- Leveraging Discrete-Event Simulation and Scene Graphs for Modeling Hierarchical, Interconnected Environments under Partial Observations from Mobile Agents
- 融合场景图与离散事件模拟，实现对象动态与交互的结构化建模
- 在城市场景中生成真实时空模式，揭示稀疏观测下信念估计的挑战
- 开源框架支持不确定性传播与多智能体行为研究，适合具身AI实验
动态场景图（DSGs）为层级互联环境提供了结构化表示，但现有方法难以捕捉随机动态、部分可观测性及多智能体活动。这些特性对具身AI至关重要，因智能体需在不确定性和延迟感知下行动。我们提出FOGMACHINE，一个开源框架，将DSGs与离散事件模拟结合，实现对象动态、智能体观测与交互的规模化建模。该设置支持不确定性传播、有限感知下的规划以及涌现的多智能体行为研究。城市场景实验展示了真实的时间与空间模式，并揭示了在稀疏观测下信念估计的挑战。通过结合结构化表示与高效模拟，FOGMACHINE成为复杂不确定环境中具身AI基准测试、模型训练与推进的有效工具。
原文摘要 · Abstract (English)
Dynamic Scene Graphs (DSGs) provide a structured representation of hierarchical, interconnected environments, but current approaches struggle to capture stochastic dynamics, partial observability, and multi-agent activity. These aspects are critical for embodied AI, where agents must act under uncertainty and delayed perception. We introduce FOGMACHINE , an open-source framework that fuses DSGs with discrete-event simulation to model object dynamics, agent observations, and interactions at scale. This setup enables the study of uncertainty propagation, planning under limited perception, and emergent multi-agent behavior. Experiments in urban scenarios illustrate realistic temporal and spatial patterns while revealing the challenges of belief estimation under sparse observations. By combining structured representations with efficient simulation, FOGMACHINE establishes an effective tool for benchmarking, model training, and advancing embodied AI in complex, uncertain environments.
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