Abstract
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, we propose two vision-based, multiobject grasp-pose estimation models, the MOGPE Real-Time (RT) and the MOGPE High-Precision (HP). Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. We yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE RT and the MOGPE HP model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data.
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URL
https://arxiv.org/abs/2211.01048
