LEARNING
基于物理的多智能体动力学世界模型基准
Nuo Chen, Lulin Liu, Zihao Li, Ziyao Zeng, Zihao Zhu, Wenyan Cong, Junyuan Hong, Yunhao Yang, Zhengzhong Tu, Yan Wang, Boris Ivanovic, Marco Pavone, Zhangyang Wang, Yang Zhou, Zhiwen Fan
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
该论文提出了CrashTwin，一个用于评估生成世界模型物理可信度的基准框架。它通过多智能体碰撞场景数据集和校准无关的重建管道，系统评估时空一致性等物理属性。
关键词
world modelsmulti-agent dynamicsphysics-grounded benchmarkcollision scenariosphysical plausibility
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