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HGCNet: Deep Anthropomorphic Hand Grasping in Clutter
Li YM(李一鸣)
2022-05
会议名称IEEE International Conference on Robotics and Automation
会议日期2022-5
会议地点线上+线下(美国费城)
出版者IEEE
摘要

Grasping in cluttered environments is one of the most fundamental skills in robotic manipulation. Most of the current works focus on estimating grasp poses for parallel-jaw or suction-cup end effectors. However, the study for dexterous anthropomorphic hand grasping in clutter remains a great challenge. In this paper, we propose HGC-Net, a single-shot network that learns to predict dense hand grasp configurations in clutter from single-view point cloud input. Our end-to-end neural network can predict hand grasp proposals efficiently and effectively. To enhance generalization, we built a largescale synthetic grasping dataset with 179 household objects, 5K cluttered scenes and over 10M hand annotations. Experiments in simulation show that our model can predict dense and robust hand grasps and clear over 78% of unseen objects in clutter without any post-processing and outperform baseline methods by a large margin. Experiments on the real robot platform also demonstrate that the model trained on synthetic data performs well in natural environments. Code is available at https://github.com/yimingli1998/hgc net.

收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/48751
专题智能机器人系统研究
作者单位1.中国科学院自动化研究所
2.中国科学院大学
推荐引用方式
GB/T 7714
Li YM. HGCNet: Deep Anthropomorphic Hand Grasping in Clutter[C]:IEEE,2022.
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