Robotic Collaboration and Autonomy Lab

Through probabilistic methods and learning techniques, RoCAL lab works to improve the performance and reliability of robotic collaborative environmental perception, motion planning, and robot learning. The research being done in this lab looks to identify the key factors that closely correlate with surgical outcomes in endoscope sinus and skull based surgeries, explores the correlation of surgical outcomes with surgical planning and surgical motions, and builds supervised, autonomous surgical robots to maximize surgical outcomes. RoCAL lab focuses on building precise and robust graphs by improving feature detection and data association reliability, adapting to environmental changes, and through collaborative mapping.