We are developing machine learning algorithms to automatically track, model, and control multibody dynamic systems. Our efficient algorithms are deployed on mobile devices and control hardware units. Applications include markerless tracking of human movements, control of autonomous vehicles, automotive powertrain models, and environment recognition systems for lower-limb exoskeletons and prostheses.
• Deep Learning and Computer Vision
• Convolutional Neural Networks
• Reinforcement Learning
• Autonomous Vehicles
• Environment Recognition Systems
• Lin Y, McPhee J, and Azad NL. (2019). Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control. arXiv:1910.12047
• McNally W, Wong A, and McPhee J. (2019). STAR-Net: Action Recognition using Spatio-Temporal Activation Reprojection. arXiv:1902.10024.
• McNally W, Vats K, Pinto T, Dulhanty C, McPhee J, and Wong A. (2019). GolfDB: A Video Database for Golf Swing Sequencing. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:1903.06528.