Social Navigation
Social navigation focuses on enabling robots to navigate safely and efficiently in environments shared with humans, respecting social norms and avoiding collisions. Current research emphasizes the development of robust perception systems using vision-language models and advanced planning algorithms like reinforcement learning, often incorporating elements of predictive modeling and uncertainty quantification to improve safety and adaptability. This field is crucial for the safe deployment of robots in public spaces and is driving advancements in areas such as human-robot interaction, multi-agent systems, and the development of realistic simulation environments for benchmarking.
Papers
CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification
Andrea Ruo, Lorenzo Sabattini, Valeria Villani
Follow me: an architecture for user identification and social navigation with a mobile robot
Andrea Ruo, Lorenzo Sabattini, Valeria Villani
CBF-Based STL Motion Planning for Social Navigation in Crowded Environment
Andrea Ruo, Lorenzo Sabattini, Valeria Villani
VLM-Social-Nav: Socially Aware Robot Navigation through Scoring using Vision-Language Models
Daeun Song, Jing Liang, Amirreza Payandeh, Xuesu Xiao, Dinesh Manocha
Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning
Daniel Flögel, Lars Fischer, Thomas Rudolf, Tobias Schürmann, Sören Hohmann
TH\"OR-MAGNI: A Large-scale Indoor Motion Capture Recording of Human Movement and Robot Interaction
Tim Schreiter, Tiago Rodrigues de Almeida, Yufei Zhu, Eduardo Gutierrez Maestro, Lucas Morillo-Mendez, Andrey Rudenko, Luigi Palmieri, Tomasz P. Kucner, Martin Magnusson, Achim J. Lilienthal