  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.
