Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection

arXiv:2607.15974v1 Announce Type: new Abstract: 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 sa...