基于RGB感知的机器人抓取中由共识驱动的不确定性
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
AI总结：
提出一种训练轻量级深度网络的方法，通过真实图像姿态估计和模拟抓取生成数据，预测基于图像姿态估计的抓取能否成功，并发现多物体联合训练有益。
摘要：
深度物体姿态估计器通常过于自信。一个同时估计目标物体的6-DoF姿态并预测其自身估计不确定性的抓取智能体，可以通过在高不确定性下选择弃权（不执行）来避免任务失败。尽管物体姿态估计有所改进，且不确定性量化研究不断取得进展，但很少有研究将它们与机器人抓取的下游任务联系起来。我们提出了一种方法，用于训练轻量级深度网络，以预测由基于图像的姿态估计引导的抓取在尝试之前是否会成功。我们通过对真实图像进行物体姿态估计和模拟抓取，为我们的网络生成训练数据。我们还发现，尽管抓取试验中物体变异性很高，网络仍能从所有物体的联合训练中受益，这表明各种各样的物体仍然可以为同一目标做出贡献。
英文摘要：
Deep object pose estimators are notoriously overconfident. A grasping agent that both estimates the 6-DoF pose of a target object and predicts the uncertainty of its own estimate could avoid task failure by choosing not to act under high uncertainty. Even though object pose estimation improves and uncertainty quantification research continues to make strides, few studies have connected them to the downstream task of robotic grasping. We propose a method for training lightweight, deep networks to predict whether a grasp guided by an image-based pose estimate will succeed before that grasp is attempted. We generate training data for our networks via object pose estimation on real images and simulated grasping. We also find that, despite high object variability in grasping trials, networks benefit from training on all objects jointly, suggesting that a diverse variety of objects can nevertheless contribute to the same goal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
