GoNet: An Approach-Constrained Generative Grasp Sampling Network
Author:
Affiliation:
1. division of Robotics, Perception, and Learning (RPL) at KTH,Stockholm,Sweden
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10375141/10374561/10375235.pdf?arnumber=10375235
Reference28 articles.
1. Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
2. Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
3. Fully Convolutional Grasp Detection Network with Oriented Anchor Box
4. On-Policy Dataset Synthesis for Learning Robot Grasping Policies Using Fully Convolutional Deep Networks
5. Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. CAPGrasp: An $\mathbb {R}^{3}\times \text{SO(2)-Equivariant}$ Continuous Approach-Constrained Generative Grasp Sampler;IEEE Robotics and Automation Letters;2024-04
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