A Cable‐Actuated Soft Manipulator for Dexterous Grasping Based on Deep Reinforcement Learning
Cable-Actuated Soft Manipulator Based on Deep Reinforcement Learning In article number 2400112, Juntian Qu and co-workers propose a type of modified TD3 (twin delayed deep deterministic policy gradient) algorithm in combination with LSTM (long short-term memory) neural networks to control the cable-driven soft manipulator. Multi-scenario and multi-task experiments are carried out based on the soft manipulator, such as precisely placing a 6 mm diameter ball into a 10 mm diameter glass bottle and accurately retrieving a shell from within an L-shaped pipe using the soft manipulator.
Authors
- Juntian Qu (ORCID: https://orcid.org/0000-0002-1799-5847)
- Yuzhu Zhang (ORCID: https://orcid.org/0009-0008-3848-7076)
- Yaozhen Chen (ORCID: https://orcid.org/0000-0003-2291-0622)
- Baijin Mao (ORCID: https://orcid.org/0009-0007-3133-1805)
- Hongwei Hao (ORCID: https://orcid.org/0000-0003-2019-516X)
- Zhenping Yu
- Houde Liu (ORCID: https://orcid.org/0000-0002-7314-3366)
- Xueqian Wang (ORCID: https://orcid.org/0000-0002-8632-6073)
- Yanwen Li (ORCID: https://orcid.org/0000-0003-3221-0945)
- Yuyaocen Xiang
- Wei Tang (ORCID: https://orcid.org/0000-0003-3091-0374)
- Kunyu Zhou
- Xiaohao Wang
Institutions
- University Town of Shenzhen (CN)
- ETH Zurich (CH)
- Tsinghua University (CN)
Publication Details
- Journal
- Advanced Intelligent Systems
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1002/aisy.202470046
- Citations
- 3
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.55