Problem
Research questions and friction points this paper is trying to address.
Air-Ground Object Search
Unmanned Aerial Vehicle
Unmanned Ground Vehicle
Vision-Language Models
Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method
🤖 AI Summary
本文针对空中-地面协作物体搜索问题，通过引入AGOS-Bench基准和AGOS-Dataset数据集，并提出无需训练的AGOS-Agent方法来提高搜索效率和成功率。
📝 Abstract
Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) to jointly search for and verify a specified target vehicle from multi-view visual references. To study this underexplored problem, we introduce AGOS-Bench, the first dedicated benchmark for evaluating whether general-purpose Vision-Language Models (VLMs) can integrate aerial discoveries and ground-level verification through UAV-UGV cooperation. We further provide AGOS-Dataset as the companion resource of exemplary trajectories constructed by an automatic pipeline. It consists of 7.7k episodes for searching objects of diverse categories and attributes, spanning three difficulty levels. To address the AGOS task, we propose AGOS-Agent, a training-free and tool-augmented approach. The agentic method relieves VLMs from complex and dynamic coordination via a deliberate search-handoff-verify cooperation protocol, only demanding VLMs for scene understanding and decision-making. Extensive experiments on nine VLMs show that AGOS-Agent improves overall success rate for eight of the nine evaluated backbones while reducing decision steps for all nine. On the hard split, the SR and SPL of Gemini-3.6-Flash increase from 8.6% to 55.7% and from 7.6% to 44.0%, respectively.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AGOS-Agent
search-handoff-verify cooperation protocol
AGOS-Bench
AGOS-Dataset
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