仅优质抓取：一种利用抓取位姿进行验证的位姿估计自监督微调数据引擎
Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification
AI总结：
针对料箱拣选场景下位姿估计方案快速部署需求，提出一种借助零样本位姿估计、用抓取位姿验证的自监督微调数据引擎，可运行中自动获取数据微调，在四物体测试中性能优于基于CAD训练的SOTA方法。
摘要：
本文提出了一种位姿估计自监督微调的新方法。该方法借助零样本位姿估计，让机器人无需人工标注即可自动获取训练数据。位姿估计完成后抓取物体，并通过手内位姿估计开展数据验证。所提流水线可让系统在运行过程中同步微调，无需单独设置学习阶段。本研究的动因源于位姿估计方案快速部署的需求，具体针对柔性机器人配置中至关重要的挑战性任务——料箱拣选展开研究。该方法在机器人工作单元上实现，并用四种不同物体进行测试。对于所有测试物体，该方法均提升了性能，且效果优于基于物体CAD模型训练的当前最优方法。项目页面见gogoengine.github.io。
英文摘要：
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. Project page available at gogoengine.github.io
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。
