Learning-based approaches to robotic manipulation are limited by the scalability of data collection and accessibility of labels. In this paper, we present a multi-task domain adaptation framework for instance grasping in cluttered scenes by utilizing simulated robot experiments. Our neural network takes monocular RGB images and the instance segmentation mask of a specified target object as inputs, and predicts the probability of successfully grasping the specified object for each candidate motor command. The proposed transfer learning framework trains a model for instance grasping in simulation and uses a domain-adversarial loss to transfer the trained model to real robots using indiscriminate grasping data, which is available both in simulation and the real world. We evaluate our model in real-world robot experiments, comparing it with alternative model architectures as well as an indiscriminate grasping baseline.
@article{arxiv.1710.06422,
title = {Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation},
author = {Kuan Fang and Yunfei Bai and Stefan Hinterstoisser and Silvio Savarese and Mrinal Kalakrishnan},
journal= {arXiv preprint arXiv:1710.06422},
year = {2018}
}