Object recognition and instance segmentation are fundamental skills in any robotic or autonomous system. Existing state-of-the-art methods are often unable to capture meaningful uncertainty in challenging or ambiguous scenes, and as such can cause critical errors in high-performance applications. In this paper, we explore a class of distributional instance segmentation models using latent codes that can model uncertainty over plausible hypotheses of object masks. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary in industrial use cases. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes in a robotic application. On a real-world apparel-picking robot, our method significantly reduces double pick errors while maintaining high performance.

翻译：目标识别与实例分割是任何机器人或自主系统中的基本技能。现有最先进方法往往难以捕捉具有挑战性或模糊场景中有意义的不确定性，从而在高性能应用中可能导致严重错误。本文探索了一类使用隐编码的分布式实例分割模型，能够对物体掩膜可能假设的不确定性进行建模。针对机器人抓取应用，我们提出了一种置信度掩膜方法，以满足工业场景所需的高精度要求。实验表明，我们的方法能显著减少机器人系统中的关键错误——包括我们新发布的机器人应用中模糊场景数据集。在真实世界的衣物抓取机器人上，该方法在保持高性能的同时大幅降低了双重抓取错误。