  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.
