Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention-Reference-Cited by-同舟云学术

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Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention

Published:2021-05-30 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Gupta Abhishek,Yu Justin,Zhao Tony Z.,Kumar Vikash,Rovinsky Aaron,Xu Kelvin,Devlin Thomas,Levine Sergey

Funder

Office of Naval Research

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9560720/9560666/09561384.pdf?arnumber=9561384

Cited by 24 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. Reset-Free Reinforcement Learning via Multi-State Recovery and Failure Prevention for Autonomous Robots;Tsinghua Science and Technology;2024-10

2. A Brain-Inspired Model of Reaching and Adaptation on the iCub Robot;2024 IEEE International Symposium on Robotic and Sensors Environments (ROSE);2024-06-20

3. RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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