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20242026
most citedOnline Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning

19 citations · 19 across the 15 of their papers we have counts for

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12 papers · 1 filter

cs.RO2026

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

Wenhao Tang, Tianyang Chen, Zhejun Cui +9

Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynamics and continuously changing…

cs.RO2026

Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents

Yixian Zhang, Huanming Zhang, Feng Gao +14

Language-conditioned manipulation requires both precise contact-rich control and robust reasoning over language, scenes, and long horizons. End-to-end Vision-Language-Action (VLA)…

cs.RO2026

LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies

Jialei Chen, Kai Wang, Kang Chen +9

Vision-Language-Action models (VLAs) leverage large-scale vision-language pretraining for semantic robot control, but often lack explicit foresight into how robot actions change th…

cs.RO2026

USER: A Unified and Extensible System for Real-World Online Policy Learning in Embodied AI

Hongzhi Zang, Shu'ang Yu, Hao Lin +14

Online policy learning directly in the physical world is a promising yet challenging direction for embodied intelligence. Unlike simulation, real-world systems cannot be arbitraril…

cs.RO2025

RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models

Hongzhi Zang, Mingjie Wei, Si Xu +15

Recent studies have demonstrated the potential of reinforcement learning (RL) to improve the task performance of vision-language-action (VLA) models through interaction. However, c…

cs.RO2025

JuggleRL: Mastering Ball Juggling with a Quadrotor via Deep Reinforcement Learning

Shilong Ji, Yinuo Chen, Chuqi Wang +9

Aerial robots interacting with objects must perform precise, contact-rich maneuvers under uncertainty. In this paper, we study the problem of aerial ball juggling using a quadrotor…

cs.RO2025

Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots

Zongyuan Chen, Yan Xia, Jiayuan Liu +10

Soft robots exhibit inherent compliance and safety, which makes them particularly suitable for applications requiring direct physical interaction with humans, such as surgical proc…

cs.RO2025

Multi-Robot System for Cooperative Exploration in Unknown Environments: A Survey

Chuqi Wang, Chao Yu, Xin Xu +11

With the real need of field exploration in large-scale and extreme outdoor environments, cooperative exploration tasks have garnered increasing attention. This paper presents a com…

cs.RO2025

VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play

Zelai Xu, Ruize Zhang, Chao Yu +9

Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we…

cs.RO2024

What Matters in Learning A Zero-Shot Sim-to-Real RL Policy for Quadrotor Control? A Comprehensive Study

Jiayu Chen, Chao Yu, Yuqing Xie +9

Executing precise and agile flight maneuvers is critical for quadrotors in various applications. Traditional quadrotor control approaches are limited by their reliance on flat traj…

cs.RO2024

Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning

Yuqing Xie, Chao Yu, Hongzhi Zang +7

This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed fli…

cs.RO2024★ 19 cited

Online Planning for Multi-UAV Pursuit-Evasion in Unknown Environments Using Deep Reinforcement Learning

Jiayu Chen, Chao Yu, Guosheng Li +6

Multi-UAV pursuit-evasion, where pursuers aim to capture evaders, poses a key challenge for UAV swarm intelligence. Multi-agent reinforcement learning (MARL) has demonstrated poten…