限られたアノテーションとナビゲーション予算下での物体検出のための身体化能動学習
Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
ロボットのナビゲーション時間とアノテーション予算の制約下で、未知環境に物体検出器を適応させる能動学習手法を提案。空間的不整合性を利用して失敗事例を効率的に選択し、AI2-THORシミュレータと実機Spotロボットで有効性を実証した。
著者: Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza, Joni Pajarinen, Riad Akrour
分類: cs.RO, cs.CV
原文アブストラクト
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