3 citations · 6 across the 17 of their papers we have counts for
17 papers
Rethinking Positional Encoding for Neural Vehicle Routing
Chuanbo Hua, Federico Berto, Andre Hottung +8
Transformer-based models have become the dominant paradigm for neural combinatorial optimization (NCO) of vehicle routing problems (VRPs), yet the role of positional encoding (PE)…
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…
JudgeFlow: Agentic Workflow Optimization via Block Judge
Zihan Ma, Zhikai Zhao, Chuanbo Hua +2
Optimizing LLM-based agentic workflows is challenging for scaling AI capabilities. Current methods rely on coarse, end-to-end evaluation signals and lack fine-grained signals on wh…
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…
VRPAgent: LLM-Driven Discovery of Heuristic Operators for Vehicle Routing Problems
André Hottung, Federico Berto, Chuanbo Hua +9
Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based…
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…
USPR: Learning a Unified Solver for Profiled Routing
Chuanbo Hua, Federico Berto, Zhikai Zhao +3
The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle-client-specific preferences and constraints, reflecting real-world requirements such…
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…
Learning Strategy Representation for Imitation Learning in Multi-Agent Games
Shiqi Lei, Kanghoon Lee, Linjing Li +1
The offline datasets for imitation learning (IL) in multi-agent games typically contain player trajectories exhibiting diverse strategies, which necessitate measures to prevent lea…
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda +6
This paper introduces RouteFinder, a comprehensive foundation model framework to tackle different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model f…
Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
Huijie Tang, Federico Berto, Jinkyoo Park
Multi-Agent Reinforcement Learning (MARL) based Multi-Agent Path Finding (MAPF) has recently gained attention due to its efficiency and scalability. Several MARL-MAPF methods choos…
ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games
Shiqi Lei, Kanghoon Lee, Linjing Li +2
Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environm…
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Huijie Tang, Federico Berto, Zihan Ma +3
Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultane…
Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation
Jiachen Li, Chuanbo Hua, Jianpeng Yao +4
Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. While modeling pairwise relat…
Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation
Jiachen Li, David Isele, Kanghoon Lee +3
Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural netwo…
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Chuanbo Hua, Federico Berto, Michael Poli +2
While complex simulations of physical systems have been widely used in engineering and scientific computing, lowering their often prohibitive computational requirements has only re…