8 papers
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
He Jiang, Jingtian Yan, Yulun Zhang +5
Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones. While m…
PRIMAL3: Pathfinding via Reinforcement and Imitation Multi-Agent Learning - Leveraging LaCAM3
Chengyang He, Tanishq Duhan, Gadiel Sznaier Camps +6
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-gu…
COIN: Collaborative Interaction-Aware Multi-Agent Reinforcement Learning for Self-Driving Systems
Yifeng Zhang, Jieming Chen, Tingguang Zhou +4
Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency i…
CAMO: A Conditional Neural Solver for the Multi-objective Multiple Traveling Salesman Problem
Fengxiaoxiao Li, Xiao Mao, Mingfeng Fan +4
Robotic systems often require a team of robots to collectively visit multiple targets while optimizing competing objectives, such as total travel cost and makespan. This setting ca…
Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild
Derek Ming Siang Tan, Shailesh, Boyang Liu +8
To perform outdoor visual navigation and search, a robot may leverage satellite imagery to generate visual priors. This can help inform high-level search strategies, even when such…
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,…