Learning Social Heuristics for Human-Aware Path Planning
This work addresses social acceptance in robotics for human-robot interaction, but it is incremental as it builds on existing navigation methods with a new learning component.
The authors tackled the problem of enabling robots to navigate in socially acceptable ways by learning social norms, specifically for joining a queue, and proposed a method that integrates learned social costs into heuristic-search path planning.
Social robotic navigation has been at the center of numerous studies in recent years. Most of the research has focused on driving the robotic agent along obstacle-free trajectories, respecting social distances from humans, and predicting their movements to optimize navigation. However, in order to really be socially accepted, the robots must be able to attain certain social norms that cannot arise from conventional navigation, but require a dedicated learning process. We propose Heuristic Planning with Learned Social Value (HPLSV), a method to learn a value function encapsulating the cost of social navigation, and use it as an additional heuristic in heuristic-search path planning. In this preliminary work, we apply the methodology to the common social scenario of joining a queue of people, with the intention of generalizing to further human activities.