MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes
<|reference_start|>MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes: This paper focuses on target-oriented grasping in occluded scenes, where the target object is specified by a binary mask and the goal is to grasp the target object with as few robotic manipulations as possible. Most existing methods rely on a push-grasping synergy to complete this task. To deliver a more powerful target-oriented grasping pipeline, we present MPGNet, a three-branch network for learning a synergy between moving, pushing, and grasping actions. We also propose a multi-stage training strategy to train the MPGNet which contains three policy networks corresponding to the three actions. The effectiveness of our method is demonstrated via both simulated and real-world experiments.<|reference_end|>
@article{li2024mpgnet:,
    title={MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping
  in Occluded Scenes},
    author={Dayou Li, Chenkun Zhao, Shuo Yang, Ran Song, Xiaolei Li and Wei Zhang},
    journal={arXiv preprint arXiv:2408.10525},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10525},
    primaryClass={cs.RO}
}