Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications
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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 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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