Robot Instance Segmentation with Few Annotations for Grasping
This addresses the challenge of reducing annotation effort for robot perception in manipulation tasks, though it appears incremental as it builds on existing SSL and LTI methods.
The paper tackles the problem of robot instance segmentation for grasping in cluttered scenes with high object variability by proposing a framework that combines Semi-Supervised Learning with Learning Through Interaction, achieving state-of-the-art performance with an AP50 of 86.37 on ARMBench, a nearly 20% improvement, and an AP50 of 84.89 with only 1% of annotated data.
The ability of robots to manipulate objects relies heavily on their aptitude for visual perception. In domains characterized by cluttered scenes and high object variability, most methods call for vast labeled datasets, laboriously hand-annotated, with the aim of training capable models. Once deployed, the challenge of generalizing to unfamiliar objects implies that the model must evolve alongside its domain. To address this, we propose a novel framework that combines Semi-Supervised Learning (SSL) with Learning Through Interaction (LTI), allowing a model to learn by observing scene alterations and leverage visual consistency despite temporal gaps without requiring curated data of interaction sequences. As a result, our approach exploits partially annotated data through self-supervision and incorporates temporal context using pseudo-sequences generated from unlabeled still images. We validate our method on two common benchmarks, ARMBench mix-object-tote and OCID, where it achieves state-of-the-art performance. Notably, on ARMBench, we attain an $\text{AP}_{50}$ of $86.37$, almost a $20\%$ improvement over existing work, and obtain remarkable results in scenarios with extremely low annotation, achieving an $\text{AP}_{50}$ score of $84.89$ with just $1 \%$ of annotated data compared to $72$ presented in ARMBench on the fully annotated counterpart.