无需提示词和预设类别，让机器人识别没见过的物体。
Identifying the Unknown: Prompt-Free Open Vocabulary Anomaly Recognition for Robot-Object Interaction
- 用自编码器生成通用边界框，再由分类器识别未知物体区域。
- 在真实桌面场景中实现57.5%的AP50，未知物体识别准确率达82.6%。
- 适合需要持续部署的开放世界机器人系统，尤其擅长处理未见物体。
机器人在现实环境中需能识别从未见过的物体。随着机器人向开放世界自主发展，对无需提示词、高效且可连续部署的开放词汇检测器的需求日益增长但仍未满足。本文提出AnomNOVIC，一种两阶段已知工作区框架，结合用于异常检测训练的掩码自编码器（MAE）与NOVIC——一种强大实时的无提示开放词汇图像分类器。MAE生成不依赖具体物体的通用边界框，使NOVIC可在无预定义候选类别列表下对显著图像区域进行分类。我们在包含NICOL人形机器人的桌面机器人-物体环境上评估AnomNOVIC，实现47.1% AP / 57.5% AP50的无提示识别性能；若提供类别候选，则提升至59.0% AP / 72.5% AP50。在额外数据集上，包括含48种独特物体的野外测试集，AnomNOVIC的无提示检测与分类准确率最高达82.6%。该结果显著超越所有对比基线，包括YOLO-World-v2、OWLv2和YOLOE。
原文摘要 · Abstract (English)
Robots operating in real-world environments must in general be able to recognize previously unseen objects. As robotic systems move toward open-world autonomy, there is a growing, yet largely unmet, need for open vocabulary object detectors that are prompt-free and efficient enough for continuous deployment. We present AnomNOVIC, a two-stage known-workspace framework that combines a masked autoencoder (MAE) trained for anomaly detection, with NOVIC, a powerful real-time prompt-free open vocabulary image classifier. The MAE produces generic object-agnostic bounding boxes, allowing NOVIC to classify salient image regions without requiring a predefined candidate class list. We evaluate AnomNOVIC against strong open vocabulary baselines in a tabletop robot-object environment featuring the NICOL humanoid robot, reaching 47.1% AP / 57.5% AP50 for prompt-free recognition, and 59.0% AP / 72.5% AP50 if class candidates are provided. Across additional datasets, including an in-the-wild test set with 48 unique objects, AnomNOVIC reaches up to 82.6% prompt-free detection and classification accuracy. These results significantly surpass all tested open vocabulary baselines, including YOLO-World-v2, OWLv2, and YOLOE.
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