原文：CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
作者：Pengying Wu, Zhejia Wen, Kang Ding, Kangjie Zhou, Chunxuan Jiao, Wei Pan, Xiangli Nie, Chang Liu
来源：arXiv cs.RO（机器人）
正文
Computer Science > Robotics
arXiv:2610.27677v1 (cs)
[Submitted on 20 Oct 2026]
Title:CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation
Authors:Pengying Wu, Zhejia Wen, Kang Ding, Kangjie Zhou, Chunxuan Jiao, Wei Pan, Xiangli Nie, Chang Liu
View a PDF of the paper titled CovNav: Conformal Prediction-Guided Adaptive Inference for Object Goal Navigation, by Pengying Wu and 7 other authors
View PDF
HTML (experimental)
Abstract:In object goal navigation (ObjectNav), an embodied agent must find a target object (e.g., “find a bed”) in a previously unseen environment, relying on spatial commonsense such as object co-occurrence. Large Language Models (LLMs) encode this commonsense, but deploying them on robots exposes a trade-off between reasoning quality and efficiency: cloud-based models incur latency and privacy risks, whereas small on-device LLMs are fast but prone to hallucinated spatial judgments. Hybrid reasoning LLMs offer both a fast non-thinking mode and a slow thinking mode, which raises a key question for embodied agents: when is slow thinking worth its cost? We propose CovNav, a training-free framework that answers this question with Conformal Prediction (CP), turning dual-process (System 1 / System 2) inference into an uncertainty-aware decision. Much like recent decision models such as Jev, CovNav casts each spatial query as a structured binary decision rather than free-form generation: a local LLM in fast mode judges whether the target is likely near an observed object, CP converts its token probabilities into a prediction set, and only ambiguous queries are escalated to slow chain-of-thought reasoning. The object-pair formulation keeps calibration tractable and supports open-set objects through off-the-shelf detectors. Experiments on Gibson and HM3D show that CovNav surpasses the strongest zero-shot baseline by 0.8 and 1.7 percentage points in success rate on HM3D v1 and v2, while invoking slow reasoning for only 12.4% of queries and retaining most of the gain of always-on thinking at a fraction of its latency. Real-robot experiments on a TurtleBot 4 further validate the approach. CovNav shows how calibrated uncertainty can decide when an embodied agent should think slowly, offering a principled form of adaptive test-time reasoning for LLM-based robots.
Comments:
13 pages, 6 figures, 7 tables
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as:
arXiv:2610.27677 [cs.RO]
(or
arXiv:2610.27677v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.27677
Focus to learn more
arXiv-issued DOI via DataCite
主题
机器人 · 人工智能 · 自然语言处理 · 计算机视觉
由「前沿雷达」于 2026-10-20 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。
评论
加载中…