Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss-Reference-Cited by-同舟云学术

Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Robotics and Automation (ICRA)
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Author:

Zhu Xinghao¹,Zhou Yefan¹,Fan Yongxiang²,Sun Lingfeng¹,Chen Jianyu¹,Tomizuka Masayoshi¹

Affiliation:

1. Mechanical Systems Control Lab,UC Berkeley,CA,USA

2. FANUC Advanced Research Lab, FANUC America Corporation,CA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9811522/9811357/09811685.pdf?arnumber=9811685

Reference43 articles.

1. Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter

2. Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

3. Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning;c;2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Computer Society,0

4. Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

5. Real-World Multiobject, Multigrasp Detection

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