Embodied Uncertainty-Aware Object Segmentation
<|reference_start|>Embodied Uncertainty-Aware Object Segmentation: We introduce uncertainty-aware object instance segmentation (UncOS) and demonstrate its usefulness for embodied interactive segmentation. To deal with uncertainty in robot perception, we propose a method for generating a hypothesis distribution of object segmentation. We obtain a set of region-factored segmentation hypotheses together with confidence estimates by making multiple queries of large pre-trained models. This process can produce segmentation results that achieve state-of-the-art performance on unseen object segmentation problems. The output can also serve as input to a belief-driven process for selecting robot actions to perturb the scene to reduce ambiguity. We demonstrate the effectiveness of this method in real-robot experiments. Website: https://sites.google.com/view/embodied-uncertain-seg<|reference_end|>
@article{fang2024embodied,
    title={Embodied Uncertainty-Aware Object Segmentation},
    author={Xiaolin Fang and Leslie Pack Kaelbling and Tom'as Lozano-P'erez},
    journal={arXiv preprint arXiv:2408.04760},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04760},
    primaryClass={cs.RO cs.AI cs.CV}
}