A novel robotic grasping method for moving objects based on multi-agent deep reinforcement learning
Authors:
- Yu HuangYHYu Huang
- Daxin LiuDLDaxin Liu
- Zhenyu LiuZLZhenyu Liu
- Ke WangKWKe Wang
- Qide WangQWQide Wang
- Jianrong TanJTJianrong Tan
- 11 September 2023
- journal article
- research article
- Published by Elsevier in Robotics and Computer-Integrated Manufacturing
- Vol. 86
Abstract
No abstract availableKeywords
Funding Information
- National Key Research and Development Program of China (2019YFB1312600)
- Key Research and Development Program of Zhejiang Province (2021C01008)
- National Natural Science Foundation of China (52075480)
This publication has 20 references indexed in Scilit:
- Dynamic grasp and trajectory planning for moving objectsAutonomous Robots, 2018
- Real-World Multiobject, Multigrasp DetectionIEEE Robotics and Automation Letters, 2018
- Velocity Decomposition Based Planning Algorithm for Grasping Moving Object2018
- Robotic grasping using visual and tactile sensingInformation Sciences, 2017
- Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates2017
- Data-Driven Grasp Synthesis—A SurveyIEEE Transactions on Robotics, 2014
- A data-driven statistical framework for post-grasp manipulationThe International Journal of Robotics Research, 2014
- A Prediction and Motion-Planning Scheme for Visually Guided Robotic Capturing of Free-Floating Tumbling Objects With Uncertain DynamicsIEEE Transactions on Robotics, 2012
- Microsurgical robotic system for vitreoretinal surgeryInternational Journal of Computer Assisted Radiology and Surgery, 2011
- Automated tracking and grasping of a moving object with a robotic hand-eye systemIEEE Transactions on Robotics and Automation, 1993