arXiv Machine Learning August 17, 2026 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

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

Read the original on arXiv Machine 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Aug 3

Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

arXiv:2603. 25464v2 Announce Type: replace-cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies for any reward function directly at test time.

By Jiajun Hu, Nuria Armengol Urpi, Jin Cheng, Stelian Coros

reinforcement-learning robotics fine-tuning

arXiv AI

3d ago

AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations

arXiv:2609.36915v1 Announce Type: cross Abstract: Aerial manipulators extend robotic manipulation into 3D workspaces that are difficult for ground-based robots to access, creating new opportunities f...

By Rui Huang, Yanlin Mu, Lidong Li, Yucong Wang, Zichen Yan, Lin Zhao

reinforcement-learning robotics multimodal benchmarks safety

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)

agents reinforcement-learning efficiency

arXiv AI

Jun 2

Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX

arXiv:2606. 01478v1 Announce Type: cross Abstract: High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing the capabilities of robot algorithms.

By Martin Schuck, Marcel P. Rath, Yufei Hua, AbhisheK Goudar, SiQi Zhou, Angela P. Schoellig

agents reinforcement-learning robotics benchmarks

arXiv Machine Learning

Jul 28

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

arXiv:2607. 23565v1 Announce Type: cross Abstract: Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticipating collision risks induced by relative motion.

By Yuchao Mei, Guohao Zhang, Luxia Ai, Haopeng Chen, Wenbing Tao

computer-vision reinforcement-learning robotics safety

arXiv Machine Learning

Jun 9

Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning

arXiv:2606. 08533v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are increasingly being deployed in logistics, service robotics, and other real-world applications, creating a growing demand for autonomous payload acquisition and delivery.

By Lixuan Jin, Bingxuan Lan, Xinyi Bao, Xiangyuan Xie, Chunjie Zhang, Zheng Chen, Tianshuo Liu, Ruijie Tian, Jinyu Ru, Gang Wang, Lei Yuan, Yang Yu

rag agents reinforcement-learning robotics fine-tuning

One email a morning, machine-written

Related stories

Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

AeroManip-VLA: Scalable Vision-Language-Action Learning for Aerial Manipulation with RL-Generated Demonstrations

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

Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

Autonomous Aerial Manipulation via Contextual Contrastive Meta Reinforcement Learning