19 citations · 22 across the 8 of their papers we have counts for
8 papers
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…
Translate Policy to Language: Flow Matching Generated Rewards for LLM Explanations
Xinyi Yang, Liang Zeng, Heng Dong +6
As humans increasingly share environments with diverse agents powered by RL, LLMs, and beyond, the ability to explain agent policies in natural language is vital for reliable coexi…
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…
CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control
Jinwei Zeng, Chao Yu, Xinyi Yang +6
City-scale traffic signal control (TSC) involves thousands of heterogeneous intersections with varying topologies, making cooperative decision-making across intersections particula…
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments
Jiayu Chen, Guosheng Li, Chao Yu +4
This paper addresses multi-UAV pursuit-evasion, where a group of drones cooperates to capture a fast evader in a confined environment with obstacles. Existing heuristic algorithms,…
MASP: Scalable GNN-based Planning for Multi-Agent Navigation
Xinyi Yang, Xinting Yang, Chao Yu +4
We investigate multi-agent navigation tasks, where multiple agents need to reach initially unassigned goals in a limited time. Classical planning-based methods suffer from expensiv…
Active Neural Topological Mapping for Multi-Agent Exploration
Xinyi Yang, Yuxiang Yang, Chao Yu +5
This paper investigates the multi-agent cooperative exploration problem, which requires multiple agents to explore an unseen environment via sensory signals in a limited time. A po…
Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation
Xinyi Yang, Shiyu Huang, Yiwen Sun +5
This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) ha…