Grasping-force optimization for multifingered robotic hands using a recurrent neural network

Youshen Xia, Jun Wang, Lo Ming Fok

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


Grasping-force optimization of multifingered robotic hands can be formulated as a problem for minimizing an objective function subject to form-closure constraints and balance constraints of external force. This paper presents a novel recurrent neural network for real-time dextrous hand-grasping force optimization. The proposed neural network is shown to be globally convergent to the optimal grasping force. Compared with existing approaches to grasping-force optimization, the proposed neural-network approach has the advantages that the complexity for implementation is reduced, and the solution accuracy is increased, by avoiding the linearization of quadratic friction constraints. Simulation results show that the proposed neural network can achieve optimal grasping force in real time.

Original languageEnglish
Pages (from-to)549-554
Number of pages6
JournalIEEE Transactions on Robotics and Automation
Issue number3
Publication statusPublished - Jun 2004
Externally publishedYes


  • Grasping-force optimization
  • Multifingered robotic hands
  • Recurrent neural networks


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