# CycleIK: Neuro-inspired Inverse Kinematics

arXiv: https://arxiv.org/abs/2307.11554 · HTML: https://mlsanity.com/paper/2307.11554/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics](https://mlsanity.com/paper/2106.01981/) (2021)
- [IKFlow: Generating Diverse Inverse Kinematics Solutions](https://mlsanity.com/paper/2111.08933/) (2021)
- [NICOL: A Neuro-inspired Collaborative Semi-humanoid Robot that Bridges Social Interaction and Reliable Manipulation](https://mlsanity.com/paper/2305.08528/) (2023)

## Built on by (most cited first, 5 of 5)

- [Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents](https://mlsanity.com/paper/2404.08825/) (2024)
- [Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation](https://mlsanity.com/paper/2407.13505/) (2024)
- [Diffusing in Someone Else's Shoes: Robotic Perspective Taking with Diffusion](https://mlsanity.com/paper/2404.07735/) (2024)
- [Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task](https://mlsanity.com/paper/2404.08424/) (2024)
- [Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot](https://mlsanity.com/paper/2509.04076/) (2025)

## Abstract (arXiv)

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.