Machine Learning for Dynamic Tracking and Control

Research Description 

We are developing machine learning algorithms to track, model, and control multibody dynamic systems. Our algorithms are deployed on mobile devices and control hardware units. Applications include markerless tracking for human movement biomechanics, control of autonomous vehicles, automotive powertrain models, and environment recognition systems for robotic lower-limb exoskeletons and prostheses.

Student Researchers 

William McNally
Yuan Lin
Brock Laschowski 
Arash Hashemi
Chris Shum 

Keywords and Themes 

• Deep Learning and Computer Vision
• Convolutional Neural Networks
• Reinforcement Learning
• Human Movement Biomechanics
• Autonomous Vehicles
• Environment Recognition Systems


Environment_Recognition