Machine Learning
Machine Learning (ML) builds a mathematical model based on a “training data” in order to make predictions or decisions to perform some specific task.
The powerful combination of robots and machine learning enables entirely new possibilities.
All Publications
Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes.
Baimukashev, D.; Zhilisbayev, A.; Kuzdeuov, A.; Oleinikov, A.; Fadeyev, D.; Makhataeva, Z.; and Varol, H. A.
Machine Learning and Knowledge Extraction, 1(3): 883–903. 2019.
Human grasping database for activities of daily living with depth, color and kinematic data streams.
Saudabayev, A.; Rysbek, Z.; Khassenova, R.; and Varol, H. A.
Vital sign monitoring utilizing Eulerian video magnification and thermography.
Aubakir, B.; Nurimbetov, B.; Tursynbek, I.; and Varol, H. A.
ChibiFace: A sensor-rich Android tablet-based interface for industrial robotics.
Nurimbetov, B.; Saudabayev, A.; Temiraliuly, D.; Sakryukin, A.; Serekov, A.; and Varol, H. A.
2015 IEEE/SICE International Symposium on System Integration (SII), pages 587-592, Dec 2015
Real-time Gesture Recognition for the High-level Teleoperation Interface of a Mobile Manipulator.
Khassanov, Y.; Imanberdiyev, N.; and Varol, H. A.
Depth image based terrain recognition for supervisory control of a hybrid quadruped.
Saudabayev, A.; Kungozhin, F.; Nurseitov, D.; and Varol, H. A.
2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), pages 1532-1537, June 2014.
