CycleIK: Neuro-inspired Inverse Kinematics

TL;DR

CycleIK presents a neuro-inspired inverse kinematics approach combining GANs and MLPs, enhanced by hybrid neuro-genetic optimization, achieving competitive accuracy and efficiency on a semi-humanoid robot platform.

Contribution

Introduces CycleIK, a novel neuro-inspired IK method integrating GANs and MLPs with hybrid optimization for improved robotic kinematics performance.

Findings

Neural models compete with state-of-the-art IK methods.

Hybrid neuro-genetic approach improves precision and reduces runtime.

Models are deployable on real robotic hardware.

Abstract

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.