用于机器人的引擎不可知图形环境 (EAGERx):基于图形的仿真机器人学习框架

IF 5.4 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Robotics & Automation Magazine Pub Date : 2024-08-08 DOI:10.1109/mra.2024.3433172
Bas van der Heijden, Jelle Luijkx, Laura Ferranti, Jens Kober, Robert Babuska
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Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning
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来源期刊
IEEE Robotics & Automation Magazine
IEEE Robotics & Automation Magazine 工程技术-机器人学
CiteScore
8.80
自引率
1.80%
发文量
100
审稿时长
>12 weeks
期刊介绍: IEEE Robotics & Automation Magazine is a unique technology publication which is peer-reviewed, readable and substantive. The Magazine is a forum for articles which fall between the academic and theoretical orientation of scholarly journals and vendor sponsored trade publications. IEEE Transactions on Robotics and IEEE Transactions on Automation Science and Engineering publish advances in theory and experiment that underpin the science of robotics and automation. The Magazine complements these publications and seeks to present new scientific results to the practicing engineer through a focus on working systems and emphasizing creative solutions to real-world problems and highlighting implementation details. The Magazine publishes regular technical articles that undergo a peer review process overseen by the Magazine''s associate editors; special issues on important and emerging topics in which all articles are fully reviewed but managed by guest editors; tutorial articles written by leading experts in their field; and regular columns on topics including education, industry news, IEEE RAS news, technical and regional activity and a calendar of events.
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