构建可复现的世界模型研究平台，支持标准化评估与持续学习。
stable-worldmodel-v1: Reproducible World Modeling Research and Evaluation
- 模块化设计提供数据采集、环境与算法基线工具
- 支持视觉与物理属性可控的环境，便于鲁棒性研究
- 已用于DINO-WM零样本鲁棒性分析，验证平台实用性
世界模型作为学习环境动态紧凑预测表示的强大范式，使智能体能够推理、规划并超越直接经验进行泛化。尽管世界模型受到广泛关注，但多数实现仍局限于特定论文，严重限制了可复用性，增加错误风险，并降低评估一致性。为此，我们提出稳定世界模型（SWM），一个模块化、经过测试且文档齐全的世界模型研究生态系统，提供高效的数据采集工具、标准化环境、规划算法和基线实现。SWM中的每个环境均支持可控的变量因素，包括视觉与物理属性，以促进鲁棒性和持续学习研究。最后，我们通过使用SWM研究DINO-WM的零样本鲁棒性，展示了该平台的实用性。
原文摘要 · Abstract (English)
World Models have emerged as a powerful paradigm for learning compact, predictive representations of environment dynamics, enabling agents to reason, plan, and generalize beyond direct experience. Despite recent interest in World Models, most available implementations remain publication-specific, severely limiting their reusability, increasing the risk of bugs, and reducing evaluation standardization. To mitigate these issues, we introduce stable-worldmodel (SWM), a modular, tested, and documented world-model research ecosystem that provides efficient data-collection tools, standardized environments, planning algorithms, and baseline implementations. In addition, each environment in SWM enables controllable factors of variation, including visual and physical properties, to support robustness and continual learning research. Finally, we demonstrate the utility of SWM by using it to study zero-shot robustness in DINO-WM.
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