用抓取验证自动生成标注数据，实现无监督姿态估计的实时优化
Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification
- 通过抓取后内视姿态验证，自动筛选有效训练数据
- 四类物体均提升精度，优于基于CAD模型训练的方法
- 适合快速部署机器人抓取系统，无需人工标注
本文提出一种自监督微调姿态估计的新方法。利用零样本姿态估计，机器人可自动获取训练数据而无需人工标注。姿态估计后对物体进行抓取，再通过手内姿态估计验证数据有效性。该流程支持运行中持续微调，无需独立学习阶段。研究聚焦于快速部署姿态估计方案，针对柔性机器人系统中的典型任务——料箱分拣展开。方法在机器人工作单元上实现，测试了四种不同物体。所有物体性能均得到提升，优于基于对象CAD模型训练的当前最优方法。
原文摘要 · Abstract (English)
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
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