Data collection towards socially inspired interactive motion planning

In public and social spaces shared by humans and robots, it is essential for both parties to be aware of each other’s goals and to communicate their intentions effectively. This work in progress aims to develop a motion planner that not only finds the shortest path to a goal while adhering to social norms and spatial constraints but also generates communicative gestures and movements, such as hesitations or prompting motions. Extensive datasets are required to teach robots socially compliant navigation and effective communication with their human counterparts. Here, we outline our data collection efforts to train such a planner and to inform the broader community about potential interactions between humans and robots in these scenarios. The primary goal of this research is to establish a foundation for user-centered design of interaction strategies and avoidance mechanisms during social navigation.