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Grasp prediction and evaluation of multi-fingered dexterous hands using deep learning

  • Published:2020-07 Issue: Volume:129 Page:103550
  • ISSN:0921-8890
  • Container-title:Robotics and Autonomous Systems
  • language:en
  • Short-container-title:Robotics and Autonomous Systems

Author:

Zhao Zengzhi,Shang Weiwei,He Haoyuan,Li Zhijun

Funder

National Natural Science Foundation of China

Youth Innovation Promotion Association CAS

Publisher

Elsevier BV

Subject

Computer Science Applications,General Mathematics,Software,Control and Systems Engineering

Reference33 articles.

1. Data-driven grasp synthesis—A survey;Bohg;IEEE Trans. Robot.,2013

2. Hand posture subspaces for dexterous robotic grasping;Ciocarlie;Int. J. Robot. Res.,2009

3. Constructing force-closure grasps;Nguyen;Int. J. Robot. Res.,1988

4. Computation of independent contact regions for grasping 3-d objects;Roa;IEEE Trans. Robot.,2009

5. From caging to grasping;Rodriguez;Int. J. Robot. Res.,2012

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1. The improved force/position hybrid control system for multi joint humanoid multi fingered dexterous hand;Journal of Mechanical Science and Technology;2024-08

2. Geometric based apple suction strategy for robotic packaging;International Journal of Agricultural and Biological Engineering;2024

3. Simultaneous Action and Grasp Feasibility Prediction for Task and Motion Planning Through Multi-Task Learning;2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2023-10-01

4. Research on Robot Grasping Based on Deep Learning for Real-Life Scenarios;Micromachines;2023-07-08

5. Planning Multi-fingered Grasps with Reachability Awareness in Unrestricted Workspace;Journal of Intelligent & Robotic Systems;2023-03
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