Summary of Robot Instance Segmentation with Few Annotations For Grasping, by Moshe Kimhi et al.
Robot Instance Segmentation with Few Annotations for Grasping
by Moshe Kimhi, David Vainshtein, Chaim Baskin, Dotan Di Castro
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel framework that combines Semi-Supervised Learning (SSL) and Learning Through Interaction (LTI) for improving visual perception in robots. The proposed method enables models to learn from partially annotated data through self-supervision and incorporates temporal context using pseudo-sequences generated from unlabeled still images. The approach is validated on two common benchmarks, ARMBench mix-object-tote and OCID, where it achieves state-of-the-art performance. Specifically, the model attains an AP50 score of 86.37 on ARMBench, which represents a 20% improvement over existing work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots better understand what they see by teaching them to learn from incomplete data and use what they already know to figure out new things. The researchers develop a new way for robots to learn that’s based on watching how the world changes around them, without needing lots of labeled training data. They test this method on two big datasets and show that it works really well. |
Keywords
» Artificial intelligence » Semi supervised
