In this paper, we present a novel method for self-supervised fine-tuning of pose estimation for bin-picking. 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.

本研究解决了姿态估计中的人工标注数据缺乏的问题，提出了一种自监督的微调方法，通过抓取姿态进行数据验证。该方法使机器人能够在运行过程中自动获取训练数据，显著提升了在箱体抓取任务中的性能，并超越了基于CAD模型训练的先进方法。

仅保留有效抓取：一种用于姿态估计自监督微调的数据引擎