Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification

• URL: http://arxiv.org/abs/2409.11512v1
• Date: Tue, 17 Sep 2024 19:26:21 GMT
• Title: Good Grasps Only: A data engine for self-supervised fine-tuning of pose estimation using grasp poses for verification
• Authors: Frederik Hagelskjær,
• Abstract summary: We present a novel method for self-supervised fine-tuning of pose estimation for bin-picking. Our approach enables the robot to automatically obtain training data without manual labeling. Our pipeline allows the system to fine-tune while the process is running, removing the need for a learning phase.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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