Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs-Reference-Cited by-同舟云学术

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Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs

Published:2023-05-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Automation (ICRA)
language:
Short-container-title:

Author:

Liang Junchi¹,Boularias Abdeslam¹

Affiliation:

1. Department of Computer Science of Rutgers University,Piscataway,New Jersey,USA,08854

Funder

NSF

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160820.pdf?arnumber=10160820

Reference36 articles.

1. Multi-step Pick-and-Place Tasks Using Object-centric Dense Correspondences

2. Deep Residual Learning for Image Recognition

3. Learning Multi-Object Dense Descriptor for Autonomous Goal-Conditioned Grasping

4. High Performance Visual Tracking with Siamese Region Proposal Network

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1. VG4D: Vision-Language Model Goes 4D Video Recognition;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. Robot Assembly Motion Parameter Determination Using Parts Function and Functional Consistency;Journal of the Japan Society for Precision Engineering;2024-01-05

3. Generation Method of Robot Assembly Motion Considering Physicality Gap Between Humans and Robots;Advances in Visual Computing;2023

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