# Learning Aerodynamics for the Control of Flying Humanoid Robots

arXiv: https://arxiv.org/abs/2506.00305 · HTML: https://mlsanity.com/paper/2506.00305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 67 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression](https://mlsanity.com/paper/2008.07281/) (2020)
- [Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics](https://mlsanity.com/paper/1909.13296/) (2019)
- [Learning to Walk and Fly with Adversarial Motion Priors](https://mlsanity.com/paper/2309.12784/) (2023)
- [Co-Design Optimisation of Morphing Topology and Control of Winged Drones](https://mlsanity.com/paper/2309.13948/) (2023)
- [Momentum Control of an Underactuated Flying Humanoid Robot](https://mlsanity.com/paper/1702.06075/) (2017)
- [From CAD to URDF: Co-Design of a Jet-Powered Humanoid Robot Including CAD Geometry](https://mlsanity.com/paper/2410.07963/) (2024)
- [A unified approach to fixed-wing aircraft path following guidance and control](https://mlsanity.com/paper/1803.05184/) (2018)
- [Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots](https://mlsanity.com/paper/2205.08301/) (2022)
- [Modeling and Control of Morphing Covers for the Adaptive Morphology of Humanoid Robots](https://mlsanity.com/paper/2207.01025/) (2022)
- [A Flexible MATLAB/Simulink Simulator for Robotic Floating-base Systems in Contact with the Ground](https://mlsanity.com/paper/2211.09716/) (2022)
- [Whole-Body Trajectory Optimization for Robot Multimodal Locomotion](https://mlsanity.com/paper/2211.12849/) (2022)

## Abstract (arXiv)

Robots with multi-modal locomotion are an active research field due to their versatility in diverse environments. In this context, additional actuation can provide humanoid robots with aerial capabilities. Flying humanoid robots face challenges in modeling and control, particularly with aerodynamic forces. This paper addresses these challenges from a technological and scientific standpoint. The technological contribution includes the mechanical design of iRonCub-Mk1, a jet-powered humanoid robot, optimized for jet engine integration, and hardware modifications for wind tunnel experiments on humanoid robots for precise aerodynamic forces and surface pressure measurements. The scientific contribution offers a comprehensive approach to model and control aerodynamic forces using classical and learning techniques. Computational Fluid Dynamics (CFD) simulations calculate aerodynamic forces, validated through wind tunnel experiments on iRonCub-Mk1. An automated CFD framework expands the aerodynamic dataset, enabling the training of a Deep Neural Network and a linear regression model. These models are integrated into a simulator for designing aerodynamic-aware controllers, validated through flight simulations and balancing experiments on the iRonCub-Mk1 physical prototype.