To perform tasks well in a new domain, one must first know something about it. This paper reports on a robot controller for navigation through unfamiliar indoor worlds. Based on spatial affordances, it integrates planning with reactive heuristics. Before it addresses specific targets, however, the system deliberately explores for high-level connectivity and captures that data in a cognitive spatial model. Despite limited exploration time, planning in the resultant model is faster and better supports successful travel in a challenging, realistic space.
FloNa: Floor Plan Guided Embodied Visual Navigation
Beijing Institute of Technology（北京理工大学）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）；Beijing Racobit Electronic Information Technology Co., Ltd.（北京睿科比特电子信息技术有限公司）
SignScene: Visual Sign Grounding for Mapless Navigation
Smart Systems Institute, National University of Singapore, 3 Research Link, 117602, Singapore.（新加坡国立大学智能系统研究所）；School of Computing, National University of Singapore, 13 Computing Drive, 117417, Singapore.（新加坡国立大学计算机学院）
MA-CoNav: A Master-Slave Multi-Agent Framework with Hierarchical Collaboration and Dual-Level Reflection for Long-Horizon Embodied VLN
Southwestern University of Finance（西南财经大学）；Department of Informatics,Universitat Hamburg, Hamburg, Germany（汉堡大学信息学院）；University of Electronic Science（电子科学大学）
