Journal
IEEE-ASME TRANSACTIONS ON MECHATRONICS
Volume 27, Issue 6, Pages 5296-5306Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2022.3178303
Keywords
Inverse kinematics (IKs); Jacobian; learning; sim-to-real; soft robots
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Funding
- University of Manchester
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This article presents an efficient learning method to solve the highly nonlinear inverse kinematic problem of soft robots. By using neural networks to learn the mapping function and Jacobian matrix of the forward kinematics, the IK problem can be solved through Jacobian-based iteration. A sim-to-real training transfer strategy is employed to make this approach more practical.
This article presents an efficient learning based method to solve the inverse kinematic (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.
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