A General Social Cost Layer for Robotic Navigation Planning

Recent advancements in indoor robot navigation have driven the increasing presence of service robots in homes and public spaces. While traditional geometric planning methods excel at obstacle avoidance, they often fail to generate human-aware trajectories, limiting their effectiveness in social environments. Social navigation aims to bridge this gap by prioritizing socially acceptable behaviours, ensuring natural human-robot coexistence. Current research predominantly focuses on local control approaches that predict human motion to enhance safety. However, in crowded environments, robots must exhibit human-like behaviour that cannot be effectively addressed through reactive control alone. An integration at the planning level is deemed necessary to prevent disruptions to social dynamics. This study introduces a novel framework that enriches robotic navigation with adaptive social considerations, specifically targeting scenarios where local control methods fall short in maintaining social compliance. Our approach builds upon the robustness of classical grid-based planners while incorporating a learning-based social cost layer. Using an encoder-decoder neural model, we generate a dynamic social cost map from positional data of individuals, environmental geometry, and the robot’s goal. This cost map and the static obstacle map are integrated into the planning process, allowing the robot to navigate complex social settings without compromising the default planner functionality. We validate our method across diverse real-world and simulated scenarios, including queuing, group conversations, narrow blind passages, and corridor navigation. The results demonstrate the framework’s adaptability, robustness, and ability to generalize to different social contexts, ensuring socially aware robot navigation in dynamic environments.