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
OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation
Siddarth Narasimhan, Aaron Hao Tan, Daniel Choi, Goldie Nejat
Human-Robot Cooperative Distribution Coupling for Hamiltonian-Constrained Social Navigation
Weizheng Wang, Chao Yu, Yu Wang, Byung-Cheol Min
From Cognition to Precognition: A Future-Aware Framework for Social Navigation
Zeying Gong, Tianshuai Hu, Ronghe Qiu, Junwei Liang