4 citations · 7 across the 51 of their papers we have counts for
8 papers · 1 filter
DARE: Diffusion Policy for Autonomous Robot Exploration
Yuhong Cao, Jeric Lew, Jingsong Liang +2
Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the curren…
Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
He Jiang, Yutong Wang, Rishi Veerapaneni +3
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently,…
IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity
Derek Ming Siang Tan, Yixiao Ma, Jingsong Liang +3
Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be…
HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks
Jingsong Liang, Yuhong Cao, Yixiao Ma +2
In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous explorati…
Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas
Chengyang He, Tanishq Duhan, Parth Tulsyan +2
The Multi-agent Path Finding (MAPF) problem involves finding collision-free paths for a team of agents in a known, static environment, with important applications in warehouse auto…
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning
Mingliang Zhang, Sichang Su, Chengyang He +1
In multi-agent reinforcement learning (MARL), achieving multi-task generalization to diverse agents and objectives presents significant challenges. Existing online MARL algorithms…
Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration
Yixiao Ma, Jingsong Liang, Yuhong Cao +2
Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduc…
LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding
Yutong Wang, Tanishq Duhan, Jiaoyang Li +1
Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known enviro…