Embodied Active Learning Under Limited Annotation and Navigation Budget for Object Detection
This paper presents an embodied active learning method that adapts object detectors to unknown environments while operating under constraints on both robot navigation time and annotation budget. The approach leverages spatial inconsistency to select informative trajectories and images, targeting failure cases to improve model performance. Experiments in both simulated (AI2-THOR) and real-world (Boston Dynamics Spot robot with YOLOv5) settings demonstrate that the method achieves higher detection accuracy than baseline approaches under the same resource constraints.
Why it matters: The work offers a practical solution for efficiently adapting robotic vision systems to new environments with limited resources, potentially improving real-world deployment of autonomous robots.
Full story at: arXiv Robotics ↗