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arXiv AI

Autonomous fixed-wing interception using reinforcement learning

The paper presents a fixed‑wing interceptor drone that uses a neural network trained with reinforcement learning to map monocular vision inputs directly to actuator commands. The policy, developed in a custom Python environment with proximal policy optimization, was tested against classical guidance controllers and demonstrated superior performance in simulation and successful interception of maneuvering fixed‑wing targets in real flight. This work illustrates the feasibility of AI‑driven autonomous control for long‑endurance interception missions.

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arXiv Machine Learning
Aug 27

BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning

BVR Sim is an open‑source, Gymnasium‑style environment for heterogeneous air‑combat reinforcement learning, supporting multiple JSBSim aircraft models (F‑15, F‑16, F/A‑18, F‑22) with configurable weapons, sensors, and opponents. It offers a unified tactical action interface, interchangeable Python and accelerated C++ backends, entity‑oriented observations, compositional rewards, scripted opponents, replay and visualization, and adapters for multi‑agent learning frameworks. At a 0.4‑second decision interval, the C++ backend achieves 104 simulated seconds per wall‑clock second in 1‑vs‑1 and remains practical through 10‑vs‑10 scenarios, and a policy trained on the F‑16 transfers to four unseen aircraft with a 45.5% mean win rate after controller adaptation.

By Haocheng Sun (Beijing University of Posts,Telecommunications), Mulai Tan (Air Force Engineering University)
agentsreinforcement-learningefficiency
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arXiv AI
Jul 3

Lightweight Safe Reinforcement Learning for End-to-End UAV Navigation

arXiv:2607. 01794v1 Announce Type: cross Abstract: With the rapid development of autonomous aerial systems, Unmanned Aerial Vehicles (UAVs) are increasingly deployed in applications such as inspection, environmental monitoring, and rescue, creating growing demand for reliable autonomous navigation.

By Shenghui Zhang, YuXuan Gao, Songwei Zhao, Jifeng Hu, Zijing Zhang, Hechang Chen
agentsreinforcement-learningsafety
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arXiv Machine Learning
Aug 17

AgilePE: Autonomous UAV Pursuit-Evasion via Self-Play Reinforcement Learning

arXiv:2608. 14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing opponent behaviors.

By Wenhao Tang, Tianyang Chen, Zhejun Cui, Boyuan An, Jiayu Chen, Ruize Zhang, Huidong Liu, Tianyue Wu, Qingmin Liao, Fei Gao, Yu Wang, Chao Yu
agentsreinforcement-learningrobotics
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arXiv Machine Learning
Jul 21

Hierarchical Reinforcement Learning for Air Combat at DARPA's AlphaDogfight Trials

arXiv:2105. 00990v3 Announce Type: replace Abstract: Autonomous control in high-dimensional, continuous state spaces is a persistent and important challenge in the fields of robotics and artificial intelligence.

By Adrian P. Pope, Jaime S. Ide, Daria Micovic, Henry Diaz, David Rosenbluth, Lee Ritholtz, Jason C. Twedt, Thayne T. Walker, Kevin Alcedo, Daniel Javorsek
agentsreinforcement-learningrobotics
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arXiv Machine Learning
Sep 10

FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite

FALCON‑S is a modular, high‑fidelity simulator designed for fixed‑wing aerial robots operating near the ground. It models full 6DoF rigid‑body physics, semi‑empirical ground‑effect aerodynamics, actuator dynamics, sensor noise, and environmental disturbances, and supports both CPU and GPU backends via Torch and NVIDIA Warp for large‑scale reinforcement learning and optimal control. The framework offers a unified interface for various controllers, including RL and optical control algorithms, and allows cross‑validation with X‑Plane and JSBSim for engineering integration and visual fidelity.

By Matteo El Hariry, Pedro Lima, Andrej Orsula, Matthieu Geist, Miguel Olivares-Mendez
reinforcement-learningroboticsefficiencybenchmarks
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arXiv Machine Learning
Sep 14

Curriculum-Based Adversarial Heterogeneous Agent Reinforcement Learning for Autonomous Quad-Copter Landing in Maritime Settings

The paper presents a curriculum‑based adversarial heterogeneous agent reinforcement learning (HARL‑AC) approach for autonomous quad‑copter landing on a ship deck in maritime settings. Using Heterogeneous‑Agent Proximal Policy Optimization (HAPPO) in NVIDIA Isaac Lab, the authors train a cooperative control policy that outperforms domain‑randomized baselines, achieving up to 97.5% success on in‑distribution sea states and higher median success and lower crash rates on out‑of‑distribution sea states. The adversarially trained policy also exhibits more cautious behavior, slightly increasing timeouts but improving safety in severe, unseen conditions.

By Allan Minh-Tam Nguyen, Sree Showrya Kotala, Stefan Banioi-Crijman, Kurt Driessens, Rico M\"ockel
agentsreinforcement-learningroboticsbenchmarkssafety
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arXiv AI
Sep 18

VLN on the Fly: An Onboard Vision-Language Navigation Stack for Aerial Robots

The paper introduces VLN on the Fly, an onboard vision‑language navigation stack for aerial robots that separates grounding, planning, and control into inspectable stages. A quantized vision‑language model grounds instructions to a coarse image cell, depth estimation lifts this to a 3D goal, a fast B‑spline planner generates a feasible trajectory, and a pretrained reinforcement learning policy translates the trajectory into motor commands. In controlled indoor flights, the stack achieved the target in 13 of 15 trials with a mean goal error of 5.72 cm and 39.3% GPU utilization, and successfully tracked collision‑free trajectories in cluttered environments.

By Marco S. Tayar, Felipe Tommaselli, Gianluca Capezutto, Pedro Antonio Rabelo Saraiva, Pedro H. V. de Freitas, Lucas Kido, Guilherme Sonego, Ricardo V. Godoy, Marcelo Becker
reinforcement-learningroboticsefficiencymultimodalsafety
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