Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation-Reference-Cited by-同舟云学术

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Sim-to-Real Grasp Detection with Global-to-Local RGB-D Adaptation

Published:2024-05-13 Issue: Volume: Page:13910-13917
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
language:
Short-container-title:

Author:

Ma Haoxiang¹,Qin Ran¹,Shi Modi¹,Gao Boyang²,Huang Di¹

Affiliation:

1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University,Beijing,China

2. Geometry Robotics and the School of Computer Science and Technology, Harbin Institute of Technology,Harbin,China

Funder

National Natural Science Foundation of China

State Key Laboratory of Software Development Environment

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10611165.pdf?arnumber=10611165

Reference46 articles.

1. Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours

2. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection

3. Jacquard: A Large Scale Dataset for Robotic Grasp Detection

4. REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter

5. ACRONYM: A Large-Scale Grasp Dataset Based on Simulation

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