arXiv cs.AI· Aalap D. Shah, Basil Saeed, Joshua B. Tenenbaum, Tobias Gerstenberg, Hyowon Gweon, Ilker Yildirim·· 10 小时前AI 评分34
GenEA：理解具身计划的计算框架
A computational framework for understanding embodied plans
AI 导读
研究者提出 GenEA，一种可对多步骤、长时程具身计划进行推理的智能体表示，核心是多级状态空间，将关节身体的粗粒度生物力学与物体交互耦合，并把机器人领域的任务与运动规划框架扩展至基于仿真的直觉物理。
正文 · 原文
Abstract:Reasoning about people's plans and goals based on their actions is fundamental to human social interaction and essential for cooperative, embodied artificial intelligence. Formalizing this capacity in naturalistic, embodied settings has been a longstanding challenge. Here, we present GenEA, a novel agent representation capable of reasoning over multi-step, long-horizon embodied plans. At the heart of GenEA lies a multilevel state-space that couples the coarse biomechanics of an articulated body and its interactions with nearby objects by extending the task-and-motion planning framework from robotics with simulation-based intuitive physics. By embedding this state-space in a generative model, GenEA yields a joint probability distribution over goals and embodied plans. Across several experiments spanning the qualitatively distinct domains of object manipulation and navigation, featuring diverse tasks and multiple behavioral measures, we show that GenEA, through simple transformations of this joint distribution, recapitulates how humans reason about embodied plans. Crucially, GenEA consistently outperforms strong alternative models, including plausible heuristics, standard task-and-motion planning, and a state-of-the-art vision-language model. GenEA thereby offers a new perspective on the architecture of human reasoning and social cognition, suggesting that embodied artificial intelligence systems seeking to seamlessly interact with humans should adopt GenEA-like generative models of action planning.
| Subjects: | Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Robotics (cs.RO) |
| Cite as: | arXiv:2611.13669 [cs.AI] |
| (or arXiv:2611.13669v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13669 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aalap D Shah [view email]
[v1]
Tue, 10 Nov 2026 07:15:38 UTC (47,846 KB)
来源：arXiv cs.AI · arxiv.org