Human Pose Detection for Robot Societial Navigation

Student: Nicholas Cabrales, Calin Weir

Faculty Mentor: Gurman Gil

Computer Science

College of Science, Technology, and Business

Our project aims to detect a human in an image and recognize their pose using a YOLO-based classification model, chosen for its speed and efficiency. The output of the model will help the robot interpret social cues and adjust its movements accordingly, ensuring it navigates indoor environments without invading people's personal space. This socially aware behavior is intended to reduce frustration and improve human-robot interactions in shared spaces.