arXiv cs.AI· Qinchuan Cheng, Zhantao Gong, Pengzhan Sun, Angela Yao, Xulei Yang, Shijie Li·· 10 天前AI 评分12
Ego2World：将第一视角烹饪视频编译为可执行世界以进行信念状态规划
Ego2World: Compiling Egocentric Cooking Videos into Executable Worlds for Belief-State Planning
AI 导读
Ego2World 是一个可执行基准，将第一视角烹饪视频转化为由图转移规则驱动的符号世界，基于 HD-EPIC 从视频标注中提取可复用转移规则，并在隐藏符号世界图中执行。评估时模拟器维护隐藏世界图，智能体仅凭局部观察和执行反馈在自身部分信念图上规划，无法观测真实世界状态。实验显示动作重叠分数会高估物理状态成功率，而持久信念记忆能提升任务完成率并减少重复视觉探索。
正文 · 原文
This paper has been withdrawn by Qinchuan Cheng
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Abstract:Embodied agents in household environments must plan under partial observation: they need to remember objects, track state changes, and recover when actions fail. Existing benchmarks only partially test this ability. Egocentric video datasets capture realistic human activities but remain passive, while interactive simulators support execution but rely on synthetic scenes and hand-crafted dynamics, introducing a sim-to-real gap and often assuming fully observable state. We introduce Ego2World, an executable benchmark that turns egocentric cooking videos into executable symbolic worlds governed by graph-transition rules. Built on HD-EPIC, Ego2World derives reusable transition rules from video annotations and executes them in a hidden symbolic world graph. During evaluation, the simulator maintains the hidden world graph, while the agent plans over its own partial belief graph using only local observations and execution feedback. This separation forces agents to update memory and replan without observing the true world state. Experiments show that action-overlap scores overestimate physical-state success, and that persistent belief memory improves task completion while reducing repeated visual exploration -- suggesting that belief maintenance should be a first-class target of embodied-agent evaluation.
| Comments: | I have a new version(huge different from previous one),and already put it in arXiv, so I need to withdraw previous version to avoid two articles held on arXiv in the sometime which may cause confusing |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2605.13335 [cs.AI] |
| (or arXiv:2605.13335v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13335 arXiv-issued DOI via DataCite |
Submission history
From: Qinchuan Cheng [view email]
[v1]
Wed, 13 May 2026 10:53:15 UTC (3,617 KB)
[v2]
Tue, 6 Oct 2026 01:22:36 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org