Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification
<|reference_start|>Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification: 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.<|reference_end|>
@article{hagelskjær2024good,
    title={Good Grasps Only: A data engine for self-supervised fine-tuning of pose
  estimation using grasp poses for verification},
    author={Frederik Hagelskj{ae}r},
    journal={arXiv preprint arXiv:2409.11512},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11512},
    primaryClass={cs.RO cs.CV}
}