Machine Learning for Robotics/Manufacturing Applications:

Teleoperated Robotic Surgery via Transfer Learning:

In austere environments, teleoperated surgical robots could save the lives of critically injured patients if they can perform complex surgical maneuvers under limited communication bandwidth. The bandwidth requirement is reduced by transferring atomic surgical actions (referred to as “surgemes") instead of the low-level kinematic information. While such a policy reduces the bandwidth requirement, it requires accurate recognition of the surgemes. In this work, we demonstrate that transfer learning across surgical tasks can boost the performance of surgeme recognition.


Machine Learning for Manufacturing:

Today's manufacturing starts with CAD models. The CAD model carries all the geometrical information of the workpiece (e.g., surface position, size, fluctuations etc.) and influences the entire planning for manufacturing processes. Due to the complexity of an engineering CAD model, the manufacturing industry usually decomposes the CAD model into several geometrical features - called the machining features. The machining features serve certain functions of the final product and also require typical machining processes. This CAD model decomposition and feature-recognition processes together are called machining feature identification, which is the focus of our work, where efficient data representation techniques and machine learning approaches are used.


Home