In this paper, we present a novel method for self-supervised fine-tuning of pose estimation. Leveraging zero-shot pose estimation, our approach enables the robot to automatically obtain training data without manual labeling. After pose estimation the object is grasped, and in-hand pose estimation is used for data validation. Our pipeline allows the system to fine-tune while the process is running, removing the need for a learning phase. The motivation behind our work lies in the need for rapid setup of pose estimation solutions. Specifically, we address the challenging task of bin picking, which plays a pivotal role in flexible robotic setups. Our method is implemented on a robotics work-cell, and tested with four different objects. For all objects, our method increases the performance and outperforms a state-of-the-art method trained on the CAD model of the objects.

翻译：本文提出一种用于自监督位姿估计微调的新方法。通过利用零样本位姿估计技术，我们的方法使机器人能够自动获取训练数据而无需人工标注。在完成位姿估计后对物体执行抓取操作，并利用手内位姿估计进行数据验证。我们的流水线允许系统在运行过程中实时微调，从而消除了传统学习阶段的需求。本研究的核心动机源于对快速部署位姿估计解决方案的迫切需求。我们特别针对箱体拣选这一具有挑战性的任务展开研究，该任务在柔性机器人工作站中具有关键作用。我们的方法在机器人工作站上实现，并使用四种不同物体进行测试。对于所有测试物体，我们的方法均提升了性能表现，并超越了基于物体CAD模型训练的最先进方法。