Embodied Uncertainty-Aware Object Segmentation

• URL: http://arxiv.org/abs/2408.04760v1
• Date: Thu, 8 Aug 2024 21:29:22 GMT
• Title: Embodied Uncertainty-Aware Object Segmentation
• Authors: Xiaolin Fang, Leslie Pack Kaelbling, Tomás Lozano-Pérez,
• Abstract summary: We introduce uncertainty-aware object instance segmentation (UncOS) and demonstrate its usefulness for embodied interactive segmentation. We obtain a set of region-factored segmentation hypotheses together with confidence estimates by making multiple queries of large pre-trained models. The output can also serve as input to a belief-driven process for selecting robot actions to perturb the scene to reduce ambiguity.
• Score: 38.52448300879023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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