arXiv：cs.AI· Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani, Daniele Nardi·· 3 小时前
iAm.md：通过智能体内省实现未知开放词汇领域的机器人技能自评估
iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary Domains
AI 导读
研究团队提出 iAm.md——一种 Markdown 标准与生成框架，通过开放词汇语义建图将视觉语言检测与物体分割关联到持久对象记录，使机器人智能体能够进行内省并实现技能自评估。在仿真 TIAGo 上的导航与操作任务测试表明，该标准化表示可同时支持技能自评估与可执行任务泛化，论文已被 AIRO 2026 接收。
正文
Abstract:Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks. For example, embodied agents based on Foundation models can generate plausible plans in autonomous robotics scenarios. Due to limited context windows or hallucinatory phenomena in the next-token prediction formulation, behaviors may be generated without establishing whether the deployed robot and the observed environment actually support the requested operation, in what we call a "grounding failure". Thanks to the recent improvements in reasoning capabilities of foundation models, autonomous robot behavior generation problem can be formulated as a code generation problem. We present this http URL, a Markdown standard and generation framework, that allows anchoring this process in complementary forms of deployment evidence. Through open-vocabulary semantic mapping, we combine local vision-language detections and object segmentation and refer them to persistent object records in this intermediate standardized representation, allowing agentic introspection. We then study this new technique on a simulated TIAGo, on navigation-and-manipulation tasks, showing how this standardized representation jointly supports skill self-assessment and executable task generalization.
| Comments: | 7 pages, 2 figures, 1 table. Accepted at the 13th Italian Workshop on Artificial Intelligence and Robotics (AIRO 2026). Project page: this https URL |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.10962 [cs.RO] |
| (or arXiv:2610.10962v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10962 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vincenzo Guarino [view email]
[v1]
Wed, 7 Oct 2026 22:28:28 UTC (1,396 KB)
来源：arXiv：cs.AI · arxiv.org