6 papers
EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great promise for this problem, the…
Cooperative Informative Sensing for Monitoring Dynamic Indoor Environments via Multi-Agent Reinforcement Learning
Kanghoon Lee, Matthew M. Sato, Jinnyeong Yang +7
Monitoring human activity in indoor environments is important for applications such as facility management, safety assessment, and space utilization analysis. While mobile robot te…
Priority-Aware Multi-Robot Coverage Path Planning
Kanghoon Lee, Hyeonjun Kim, Jiachen Li +1
Multi-robot systems are widely used for coverage tasks that require efficient coordination across large environments. In Multi-Robot Coverage Path Planning (MCPP), the objective is…
TrajEvo: Trajectory Prediction Heuristics Design via LLM-driven Evolution
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Trajectory prediction is a critical task in modeling human behavior, especially in safety-critical domains such as social robotics and autonomous vehicle navigation. Traditional he…
TrajEvo: Designing Trajectory Prediction Heuristics via LLM-driven Evolution
Zhikai Zhao, Chuanbo Hua, Federico Berto +4
Trajectory prediction is a crucial task in modeling human behavior, especially in fields as social robotics and autonomous vehicle navigation. Traditional heuristics based on handc…
Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm
Hyeonjun Kim, Kanghoon Lee, Junho Park +2
Multi-Agent Reinforcement Learning (MARL) has shown promise in solving complex problems involving cooperation and competition among agents, such as an Unmanned Surface Vehicle (USV…