PointNetGPD: Detecting Grasp Configurations from Point Sets

By Hongzhuo Liang, Xiaojian Ma, Shuang Li, Michael Görner, Song Tang, Bin Fang, Fuchun Sun, Jianwei Zhang. IEEE International Conference on Robotics and Automation, 2018

438 citations recorded by the source corpus.

In this paper, we propose an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud. Compared to recent grasp evaluation metrics that are based on handcrafted depth features and a convolutional neural network (CNN), our proposed PointNetGPD is lightweight and can directly process the 3D point cloud that locates within the gripper for grasp evaluation. Taking the raw point cloud as input, our proposed grasp evaluation network can capture the complex geometric structure of the contact area between the gripper and the object even if the point cloud is very sparse. To further improve our proposed model, we generate a large-scale grasp dataset with 350k real point cloud and grasps with the YCB object set for training. The performance of the proposed model is quantitatively measured both in simulation and on robotic hardware. Experiments on object grasping and clutter removal show that our proposed model generalizes well to novel objects and outperforms state-of-the-art methods. Code and video are available at https://lianghongzhuo.github.io/PointNetGPD.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/f68ff7401b39f4eb67cbe059c5e16274ac4406fb

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Citations in the corpus

GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping (2020, methodology): “For point cloud proposal method, we adopt [41, 24].”
Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes (2021, methodology): “Discriminative methods: Discriminative methods for grasping train a classifier that evaluates the quality of existing grasps [28, 29, 7].”
SQA3D: Situated Question Answering in 3D Scenes (2022, background): “, 2018) and manipulate objects (Liang et al., 2019; Savva et al., 2019; Shridhar et al., 2022; Ahn et al., 2022) following natural language commands or dialogues.”
6-DOF Grasping for Target-driven Object Manipulation in Clutter (2019, methodology): “Some recent methods tackle the more difﬁcult problem of generating grasps in SE (3) from 2D (image) [17], 2.5D (depth, multi-view) [11], [18], [19] and 3D (point cloud) [20]– [22] data.”
AnyGrasp: Robust and Efficient Grasp Perception in Spatial and Temporal Domains (2022, background): “Different sampling or evaluation models are later proposed [8], [26], [27].”