Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
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 u...
| Authors | Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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