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.

Scientific Data, 5: 180101 EP -. 05 2018.

Vital sign monitoring utilizing Eulerian video magnification and thermography.

Aubakir, B.; Nurimbetov, B.; Tursynbek, I.; and Varol, H. A.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 3527-3530, Aug 2016.

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.

Proceedings of the 2014 ACM/IEEE International Conference on Human-robot Interaction, of HRI '14, pages 204–205, New York, NY, USA, 2014. ACM

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.