HEROIC：面向开放词汇识别与跨机器人协作的异构证据推理

中文摘要 AI 辅助

多智能体异构空-地机器人团队在开放世界搜索中具有吸引力，可应用于侦察、城市搜索与救援任务（USAR）、灾难响应与恢复以及危险环境。这两种平台具有不同的失效模式：空中机器人能快速覆盖地面，但无法从高空分辨小型或被遮挡的目标，而地面机器人能在近距离识别感兴趣的目标（如人员或危险物体），但覆盖面积较小。现有的语言任务团队要么预先固定角色，要么由语言模型根据手写的能力标签分配角色，因此团队无法知道在任务中某个资产何时不再有用。我们提出了HEROIC，一个去中心化的异构多智能体开放词汇搜索协调框架，要求智能体仅通过自然语言进行通信。HEROIC的初始智能体角色分配基于传感器特性和一个尺度定律，以确定目标是否能以高置信度被检测到。仅从任务的自然语言提示中，该定律分配空中飞行高度和扫描间距。当计算出的高度低于安全飞行高度时，空中智能体将自身从搜索者重新任务为空中分诊、护航和地面智能体的路线引导者。两种机器人都对搜索区域维护一个证据信念（正证据的方位射线，负证据的对数几率后验），并对任何到达进行近距离验证的门控。在全栈实验中，HEROIC在所有6个场景中84%的时间到达目标，而视觉-语言前沿基线、前沿搜索、割草机式和随机游走（使用相同感知）的到达率为35-54%，同时到达目标的时间快2-4倍。

英文摘要

Multi-agent heterogeneous air-ground robot teams are attractive for open world search, with applications for reconnaissance, urban search and rescue missions (USAR), disaster response and recovery, and hazardous environments. These two platforms have different failure modes: aerial robots cover ground quickly but cannot resolve small or occluded targets from altitude, while ground robots can identify objects-of-interest, such as people or hazardous objects, at close range but cover less area. Existing language-tasked teams either have roles fixed prior, or have a language model assign them from hand-written capability tags, so the team is unable to know when within a mission an asset is no longer useful. We present HEROIC, a decentralized heterogeneous multi-agent open-vocabulary search coordination framework that requires agents to communicate in natural language only. HEROIC's initial agent role assignment is derived from sensor properties and a scale law to determine whether targets can be detected with a high confidence. From the mission's natural language prompt alone, this law assigns aerial flight altitudes and sweep spacing. When this calculated height falls below the altitude for safe flight, aerial agents re-task themselves from searcher to aerial triage, escort, and route guide for ground agents. Both robots maintain an evidential belief over the search area (bearing rays for positive evidence, a log-odds posterior for negative evidence) and gate any arrival on close-range verification. In full-stack experiments, HEROIC reaches the target 84% of the time across all 6 scenes, compares to 35-54% for vision-language frontier baselines, frontier-based search, lawnmower, and random-walk running the same perception, all while being 2-4x sooner to arrive at the target.

发表机构

• Purdue University（普渡大学）
• DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）

机构由 AI 辅助整理，请以论文原文为准。