This paper presents a new method to solve the inverse kinematic (IK) problem in real-time on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is caused by the lack of analytical formulation for either forward or inverse kinematics. To tackle 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 large amount of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. After that, 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 very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on several pneumatic-driven soft robots in the tasks of trajectory following and interactive positioning.
翻译:本文展示了一种新方法, 实时解决在高度非线性变形的软机器人上反动运动( IK) 的问题。 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, 我们使用神经网络学习远向运动学的绘图功能和这个函数的雅各布人。 因此, 可以用雅各语的迭代法来解决IK 问题。 为了让这个方法更加实用, 开展了一个模拟性到真实的培训转移战略。 我们首先在模拟环境中生成大量样本, 用于学习柔性机器人设计的运动和雅各语网络。 之后, 我们用一个不同神经元的光学到现实层来绘制物理硬件的模拟结果, 在那里, 从硬件上产生的培训样本数量非常有限中可以学习到这种模拟层。 我们的方法的有效性在轨迹跟踪和互动定位任务中, 已经在几个由气压驱动的软机器人上得到验证 。