在有限标注和导航预算下用于目标检测的具身主动学习
Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
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中文总结 AI 辅助
研究在机器人导航和标注预算有限时，如何让目标检测器适应未知环境。核心方法是利用空间一致性选择样本重训检测器，实验表明该方法能在无外部监督下选图，相同预算下适应结束时检测精度最高。
中文摘要 AI 辅助
本文研究如何在机器人导航时间和标注预算的双重约束下，使计算机视觉目标检测器适应未知环境。我们的方法选择信息丰富的机器人轨迹和图像样本以重新训练检测器，明确针对其失败案例。该方法是批量主动学习的具身变体，每一轮中智能体有有限导航预算收集候选样本，有有限标注预算处理最相关图像。我们利用空间一致性识别标签不一致的图像，其可能给视觉模型带来最大改进。我们在AI2-THOR模拟器的大场景以及使用波士顿动力Spot机器人和实时目标检测器YOLOv5的真实场景中，用不同主动学习目标评估该方法。通过与多个基线比较，实验结果表明空间不一致有助于引导智能体并在无外部监督下选择相关图像，在相同预算的适应过程结束时实现最高检测精度。开源项目可从此https URL获取。
英文摘要
This paper studies how to adapt a computer vision object detector to an unknown environment under both a robot navigation time and annotation budget constraint. Our approach selects informative robot trajectories and image samples to retrain the detector, explicitly targeting its failure cases. Formally, the approach is an embodied variant of batch active learning, where at each round an agent has a limited navigation budget to collect candidate samples and a limited annotation budget for the most relevant images. We leverage spatial consistency to identify images with inconsistent labels, which are likely to provide the greatest improvement to the vision model. We evaluate the approach using different active learning objectives on large scenes from the AI2-THOR simulator and on a real-world setup using a Boston Dynamics Spot robot with the real-time object detector YOLOv5. Through comparison against several baselines, our experimental results show that spatial inconsistency helps guide the agent and select relevant images without external supervision, achieving the highest detection accuracy at the end of the adaptation process under the same budget. The open-source project can be found at https://mkabouri.github.io/embodied-active-learning-od
发表机构
- LAAS-CNRS, Universite de Toulouse, CNRS（法国国家科学研究中心图卢兹分部LAAS、图卢兹大学、法国国家科学研究中心）
- Department of Electrical Engineering and Automation at Aalto University（阿尔托大学电气工程与自动化系）
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