# Learning to Grasp from a Single Demonstration

arXiv: https://arxiv.org/abs/1806.03486 · HTML: https://mlsanity.com/paper/1806.03486/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

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

- [QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity](https://mlsanity.com/paper/2410.02319/) (2024)
- [Data-efficient learning of object-centric grasp preferences](https://mlsanity.com/paper/2203.00384/) (2022)
- [Remote Task-oriented Grasp Area Teaching By Non-Experts through Interactive Segmentation and Few-Shot Learning](https://mlsanity.com/paper/2303.10195/) (2023)

## Abstract (arXiv)

Learning-based approaches for robotic grasping using visual sensors typically require collecting a large size dataset, either manually labeled or by many trial and errors of a robotic manipulator in the real or simulated world. We propose a simpler learning-from-demonstration approach that is able to detect the object to grasp from merely a single demonstration using a convolutional neural network we call GraspNet. In order to increase robustness and decrease the training time even further, we leverage data from previous demonstrations to quickly fine-tune a GrapNet for each new demonstration. We present some preliminary results on a grasping experiment with the Franka Panda cobot for which we can train a GraspNet with only hundreds of train iterations.