arXiv Machine Learning

Machine Learning-based Feedback Linearization Control of Quadrotor Subject to Unmodeled Dynamics

arXiv:2606. 31199v1 Announce Type: cross Abstract: The control of agile quadrotors in dynamic and uncertain environments remains an open area of investigation to this day, particularly when the complete system dynamics are partially known or highly nonlinear.

arXiv AI

Jun 9

Hybrid Neural Network and Conventional Controller Approach for Robust Control of Highly Unstable Systems: Application to Tilt-Rotor Control

arXiv:2606. 08714v1 Announce Type: cross Abstract: Multirotors are widely used in applications ranging from surveillance to precision agriculture, yet conventional designs remain limited by their under-actuation.

By Ali Kafili Gavgani, Amin Talaeizadeh, Aria Alasty, Hossein Nejat Pishkenari

llms

arXiv AI

Jun 15

An integrated interpretable control effectiveness learning and nonlinear control allocation methodology for overactuated aircrafts

arXiv:2606. 13794v1 Announce Type: cross Abstract: Nonlinear dynamics and the strong couplings that arise between multiple effectors undermine the assumptions behind conventional, linear control allocation techniques.

By Umut Demir, Aamir Ahmad, Walter Fichter

benchmarks safety

arXiv Machine Learning

Jul 3

Wind-Aware Reinforcement Learning Control of a Small Quadrotor Using Learned Onboard Wind Estimation in Simulated Atmospheric Turbulence

arXiv:2607. 01528v1 Announce Type: new Abstract: Small multirotor aircraft are increasingly tasked with operations in the atmospheric boundary layer, where turbulent winds comparable to the vehicle's airspeed degrade trajectory tracking and can defeat conventional feedback control.

By Abdullah Al Tasim, Wei Sun

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 19

Reinforcement Twinning for Hybrid Control of Flapping-Wing Drones

arXiv:2505. 18201v2 Announce Type: replace-cross Abstract: Controlling flapping-wing drones requires controllers that handle time-varying, nonlinear, underactuated dynamics from incomplete, noisy sensor data.

By Romain Poletti, Lorenzo Schena, Lilla Koloszar, Joris Degroote, Miguel Alfonso Mendez

agents reinforcement-learning robotics fine-tuning

arXiv AI

Jun 2

Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)

arXiv:2512. 18333v2 Announce Type: replace-cross Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors.

By Youssef Mahran, Zeyad Gamal, Ayman El-Badawy

agents reinforcement-learning

arXiv AI

Jun 2

Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)

arXiv:2512. 13356v2 Announce Type: replace-cross Abstract: This paper proposes a reinforcement learning (RL) framework for controlling and stabilizing the Twin Rotor Aerodynamic System (TRAS) at specific pitch and azimuth angles and tracking a given trajectory.

By Zeyad Gamal, Youssef Mahran, Ayman El-Badawy

agents reinforcement-learning

arXiv Machine Learning

Jun 24

SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

arXiv:2606. 23444v2 Announce Type: replace-cross Abstract: Accurate dynamics models are critical for informed decision-making in robotic systems, particularly for agile aerial vehicles operating under uncertainty.

By Pratyaksh Rao, Wancong Zhang, Randall Balestriero, Yann LeCun, Giuseppe Loianno

rag diffusion robotics

arXiv Machine Learning

Jul 7

Anticipatory Reinforcement Learning for Trajectory Tracking

arXiv:2607. 03132v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) in industrial control often suffers from lag and overshoot due to purely reactive control based on the current tracking error.

By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender

reinforcement-learning robotics

arXiv AI

Aug 10

LyEvO: Lyapunov-Guided Evolutionary Optimization for Safe and Robust Sim-to-Real Policy Learning

arXiv:2608. 06481v1 Announce Type: cross Abstract: Training controllers that are safe and robust in simulation, and systematically assessing their readiness for real-world deployment, remain key challenges in sim-to-real transfer.

By Riccardo Curcio, Hongpeng Cao, Marco Caccamo

robotics benchmarks

arXiv Machine Learning

Jun 8

Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension

arXiv:2606. 06790v1 Announce Type: cross Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution.

By Arthur Bouton, Tristan D. Hasseler, Michael Paton, Travis Brown, Jacob Levy, William Reid, Joshua Martin, Hari Nayar

agents reinforcement-learning robotics

arXiv Machine Learning

Jun 2

Autopilot-Preserving Residual Q-Learning with HJB-Inspired Finite-Action Risk Filtering for Fixed-Wing UAV Command Supervision

arXiv:2606. 01397v1 Announce Type: cross Abstract: A fixed-wing UAV must hold airspeed, altitude, and heading references under wind, gusts, and turbulence, channels coupled so that correcting one can degrade another.

By Mehmet Iscan, Batuhan Temiz

reinforcement-learning benchmarks

arXiv Machine Learning

Jul 16

Conditional Invertible Neural Networks for Data-Driven UAV Control: A 2-D Proof of Concept

arXiv:2607. 13703v1 Announce Type: new Abstract: We investigate conditional invertible neural networks (cINNs) as probabilistic inverse-dynamics models for multirotor control.

By Christian Wittke, Stephan Myschik, Oliver Niggemann

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