Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

Guoxin Fang, Yingjun Tian, Zhi Xin Yang, Jo M.P. Geraedts, Charlie C.L. Wang

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)
34 Downloads (Pure)


This article presents an efficient learning-based method to solve the <italic>inverse kinematic</italic> (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.

Original languageEnglish
Pages (from-to)5296-5306
Number of pages11
JournalIEEE/ASME Transactions on Mechatronics
Issue number6
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Computational modeling
  • Hardware
  • Inverse kinematics (IKs)
  • Jacobian
  • Jacobian matrices
  • Kinematics
  • learning
  • Numerical models
  • sim-to-real
  • Soft robotics
  • soft robots
  • Training


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