The paper introduces CycleIK, a neuro-robotic approach that wraps two novel
neuro-inspired methods for the inverse kinematics (IK) task, a Generative
Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These
methods can be used in a standalone fashion, but we also show how embedding
these into a hybrid neuro-genetic IK pipeline allows for further optimization
via sequential least-squares programming (SLSQP) or a genetic algorithm (GA).
The models are trained and tested on dense datasets that were collected from
random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a
semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the
weighted multi-objective function from the state-of-the-art BioIK method to
support the training process and our hybrid neuro-genetic architecture. We show
that the neural models can compete with state-of-the-art IK approaches, which
allows for deployment directly to robotic hardware. Additionally, it is shown
that the incorporation of the genetic algorithm improves the precision while
simultaneously reducing the overall runtime.

介绍了 CycleIK，一种神经机器人学方法，它包含了两个新颖的神经启发式反运动学方法：生成对抗网络（GAN）和多层感知机架构。研究中展示了如何将这些方法嵌入混合神经遗传反运动学流水线中，通过顺序最小二乘规划或遗传算法进行进一步的优化。该成果表明神经模型能与最先进的反运动学方法竞争，并可直接部署到机器人硬件中，同时遗传算法的引入提高了精度并减少了运行时间。