# Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

arXiv: https://arxiv.org/abs/2211.01048 · HTML: https://mlsanity.com/paper/2211.01048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Robot Instance Segmentation with Few Annotations for Grasping](https://mlsanity.com/paper/2407.01302/) (2024)
- [Removing Infrastructure Barriers in Human-Robot Collaboration Through Wireless Reconfigurable Cells](https://mlsanity.com/paper/2608.09658/) (2026)

## Abstract (arXiv)

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.