deep neural networks

Model Predictive Control and Imitation Learning Algorithms for Robot Motion Planning in Physical Human-Robot Interaction

Aigerim Nurbayeva, Postdoctoral Research Fellow, Electrical and Computer Engineering

Mar 15, 12:00 - 13:00

B9 R2325

Model Predictive Control robotics deep neural networks Numerical Optimization universal robot human-robot interactions NMPC

This seminar presents a framework for safe and efficient human-robot workspace sharing by using Deep Neural Networks (DNN) and safety filters to rapidly imitate computationally heavy Nonlinear Model Predictive Control method (NMPC), with successful experimental validation on a UR5 manipulator.

Towards Designing Robust Deep Learning Models for 3D Understanding

Abdullah Hamdi, Ph.D., Electrical and Computer Engineering

Apr 10, 17:00 - 19:00

B3 L5 R5220

deep neural networks

Deep Neural Networks (DNNs) have shown huge success over the years to solve many 2D computer vision tasks driven by massive labeled 2D datasets and advancements in 2D vision models, but less success is witnessed on 3D vision tasks. This dissertation proposes innovative approaches to enhance the robustness of DNNs for 3D understanding and in 3D settings. The research focuses on two main areas: adversarial robustness on 3D data and setups, and the robustness of DNNs to realistic 3D scenarios. Two paradigms for 3D understanding are discussed: representing 3D as a set of 3D points and performing 2D processing of multiple images of the 3D data.