Robotics and Collaborative Autonomy

Research in this industry seeks to improve the perception, inference reliability, performance, and robotics capability on autonomy and collaboration.

College of Engineering Technology faculty do this by using Bayesian learning, which connects prior knowledge with true observations and is powerful in online learning. Bayesian deep learning empowers data-driven learning techniques with the capability of dealing with causality and uncertainty.

Robotics and Collaborative Autonomy (RoCAL) uses deep Bayesian learning techniques to improve the perception, inference reliability, performance, and improve robotics capability on autonomy and collaboration through probabilistic methods and learning techniques.

Faculty

Yangming Li
Assistant Professor