A computational framework for understanding embodied plans

原文：A computational framework for understanding embodied plans

作者：Aalap D. Shah, Basil Saeed, Joshua B. Tenenbaum, Tobias Gerstenberg, Hyowon Gweon, Ilker Yildirim

来源：arXiv cs.RO（机器人）

正文

Computer Science > Artificial Intelligence

arXiv:2611.13669v1 (cs)

[Submitted on 10 Nov 2026]

Title:A computational framework for understanding embodied plans

Authors:Aalap D. Shah, Basil Saeed, Joshua B. Tenenbaum, Tobias Gerstenberg, Hyowon Gweon, Ilker Yildirim

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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

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arXiv-issued DOI via DataCite (pending registration)

主题

人工智能 · 人机交互 · 机器人

由「前沿雷达」于 2026-11-10 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。