提出新任务OVMAN，让机器人找被移动或移走的物体位置。
OVMAN: A Task and Benchmark for Open-Vocabulary Motion-Aware Navigation
- 设计双阶段导航任务，目标由场景变化定义
- 零样本导航器仅11.4%成功，空位目标几乎无法识别
- 适合研究开放词汇导航与记忆定位的学者
家居环境在机器人访问之间会发生改变。现有导航基准将目标设定为当前可见世界中的位置，而现有的双访基准仅评估召回或重排，无法表达‘前往被移动的椅子’或‘去花瓶原来的位置’这类指令。OVMAN是一个任务，其中智能体先巡视场景，经历预设变化后返回，需根据变化本身导航至目标。六个变化关系中有两个涉及物体离开后的空位，此时无物可检测。我们发布219个双访场景，每个均由一个能完成三次的预言机智能体认证为可解。两个公开系统表现如预测般失败。零样本物体目标导航器在11.4%的场景中到达变化定义的目标，对空位目标几乎失效——原位和移除目标分别得分为0.000和0.025。一个自维护的开放词汇地图在回答过去时态查询时，成功率约为两次访问地图的三分之一，即使提供定位信息亦如此。一个简单的双访参照智能体在导航上达到45.2%，而具身预言机为99.5%。错误分解表明，剩余困难主要源于开放词汇实例定位，而非几何理解或答案选择。
原文摘要 · Abstract (English)
Homes change between a robot's visits. Navigation benchmarks pose their goals in the world the agent currently sees, and the two-visit benchmarks that exist score recall or rearrangement rather than navigation. None of them can express go to the chair that was moved or go to where the vase used to be. OVMAN is a task in which an agent tours a scene, returns after a scripted change, and must navigate to a goal specified by the change itself. Two of its six change relations answer with a place an object has left, where nothing remains to be detected. We release 219 two-visit episodes, each certified solvable by an oracle agent that completes it three times. Two released systems fail as predicted. A zero-shot object-goal navigator reaches the change-defined target in 11.4% of episodes and essentially fails at vacated locations, 0.000 on former and 0.025 on removed. A self-maintaining open-vocabulary map answers past-tense queries at about a third of the rate of the same maps read as two visits, even when grounding is supplied. A simple two-visit reference agent reaches 45.2% when navigating, against an embodied oracle of 99.5%. An error decomposition places the remaining difficulty in open-vocabulary instance grounding rather than in geometry or in selecting the answer once positions are known.
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