Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.
@article{arxiv.2211.01048,
title = {Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications},
author = {Dániel Horváth and Kristóf Bocsi and Gábor Erdős and Zoltán Istenes},
journal= {arXiv preprint arXiv:2211.01048},
year = {2024}
}
Comments
Accpeted for publication in the proceedings of the 22nd IFAC World Congress