构建可规模化生成真实家庭数据的框架，实现人与环境双向影响模拟。
Realistic Synthetic Household Data Generation at Scale
- 通过人设与环境解耦生成，实现行为与环境互构。
- 合成数据与真实数据相似度达0.60，显著高于基线(0.27)。
- 支持自然语言配置，适合智能家居系统研发测试。
基础模型的发展推动了具身智能研究，旨在开发具备环境推理与交互能力的智能体。这类智能体的训练依赖多样且大规模的数据集。以往框架虽能生成长期人机交互的合成数据，却未能建模人类行为与家庭环境间的双向影响。本文提出一种生成框架，通过松耦合方式在大规模下生成人机交互与环境数据：人类角色影响环境生成，而环境结构与语义又反过来塑造人机交互。生成的3D数据包含丰富的静态上下文（如物体与环境语义）及时间上下文（捕捉长期的人类与智能体行为）。该灵活工具支持用户通过自然语言提示定义数据集特征，实现对环境与人类活动数据的自然语言配置，并生成多种变体以实现可扩展生成。我们采用多模态嵌入进行统计评估，使用余弦相似度、互信息增益、干预分析和迭代改进验证等关键指标。统计结果显示，合成数据与真实数据集（HOMER）的余弦相似度为0.60，优于已有合成数据（Wang et al.）的0.27；针对年龄、组织性、睡眠模式变化的干预分析显示显著效应（p < 0.001），效应量中等至较大（Cohen's d = 0.51–1.12），证实双向耦合有效将人格特征转化为可观测的环境与行为差异。该框架支持家庭智能设备的大规模开发与测试。
原文摘要 · Abstract (English)
Advancements in foundation models have catalyzed research in Embodied AI to develop interactive agents capable of environmental reasoning and interaction. Developing such agents requires diverse, large-scale datasets. Prior frameworks generate synthetic data for long-term human-robot interactions but fail to model the bidirectional influence between human behavior and household environments. Our proposed generative framework creates household datasets at scale through loosely coupled generation of long-term human-robot interactions and environments. Human personas influence environment generation, while environment schematics and semantics shape human-robot interactions. The generated 3D data includes rich static context such as object and environment semantics, and temporal context capturing human and agent behaviors over extended periods. Our flexible tool allows users to define dataset characteristics via natural language prompts, enabling configuration of environment and human activity data through natural language specifications. The tool creates variations of user-defined configurations, enabling scalable data generation. We validate our framework through statistical evaluation using multi-modal embeddings and key metrics: cosine similarity, mutual information gain, intervention analysis, and iterative improvement validation. Statistical comparisons show good alignment with real-world datasets (HOMER) with cosine similarity (0.60), while synthetic datasets (Wang et al.) show moderate alignment (0.27). Intervention analysis across age, organization, and sleep pattern changes shows statistically significant effects (p < 0.001) with large effect sizes (Cohen's d = 0.51-1.12), confirming bidirectional coupling translates persona traits into measurable environmental and behavioral differences. These contributions enable development and testing of household smart devices at scale.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。