学習によるソフトロボットの効率的なヤコビアン逆運動学とsim-to-real転移
Efficient Jacobian-Based Inverse Kinematics with Sim-to-Real Transfer of Soft Robots by Learning
ニューラルネットで順運動学とそのヤコビアンを学習し、ヤコビアン反復でソフトロボットの逆運動学を解く手法を提案。微分可能なsim-to-real層で実機に転移し、空圧駆動ソフトロボットの軌道追従と対話的位置決めで有効性を検証した。
著者: Guoxin Fang, Yingjun Tian, Zhi-Xin Yang, Jo M. P. Geraedts, Charlie C. L. Wang
分類: cs.RO
原文アブストラクト
This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear 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. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning.
