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.

翻译：本文介绍了一种高效的学习方法,以解决在高度非线性变形的软机器人上反动动(IK)问题。高效计算这类机器人的IK的主要挑战在于缺乏前向或反向运动学的分析配方。为了应对这一挑战,我们使用神经网络来学习远向运动学的绘图功能和这个函数的雅各哲人。因此,可以应用雅各基的迭代法来解决IK问题。为了使这一方法更加实用,实施了模拟到实际的培训转移战略。我们首先在模拟环境中生成了大量样本,用于学习柔性机器人设计的运动和雅各布网络。之后,将使用一个可不同神经元的模拟到现实层来绘制物理硬件的模拟结果,从这些物理硬件上产生的培训样本数量非常有限,可以学习这种模拟到现实的层。我们的方法的有效性已经通过气动软机器人进行验证,以便跟踪和交互定位。