MIT Department/Faculty Supervisor(s):
Dr.Hebah ElGibreen is an assistant professor at the Department of Information Technology, College of Computer and Information Sciences, King Saud University. She received her M.Sc and PhD degrees from King Saud University in 2009 and 2015, respectively. During her graduate studies, Dr.Hebah has developed a strong interest in artificial intelligence and Machine Learning. Through her master’s studies she developed algorithms that can detect errors in medical prescriptions using data mining techniques; and during her PhD she developed a knowledge-extraction algorithms that can be used as the brain of a collaborative expert system.
She started her career in 2007 by working as a teacher assistant at NXT LEGO robot program for gifted girls, sponsored by “King Abdul-Aziz & His Companions Foundation for the Gifted” program. She started to work in academia from 2008, where she played different roles as a member of the College Advising Council, head of the College Partnerships Activation Committee, head of the Department Academic Advising Committee, and a member of the College Students Rights Committee.
She has participated in a number of conferences and conducted different training sessions and workshops. She published many conferences and journals papers and was awarded for her work, where she won different awards on the academic and professional level. In 2007, she won the 2nd place of “educating by computers” track in the National Competition in Computer Skills, sponsored by College of Telecom and Information. In 2014, she got a grant from King Abdulazaiz City For Science & Technology to fund her research. In 2016 she won the third place of the Excellence in Research Activity Contest: Best PhD Track. She was also awarded as the Best Instructor and was voted to be the Best Academic Adviser at her department in the year of 2017.
Robots are becoming more common and widespread around the world. Collaborative robotic is one area that has been recently under the spot light, especially in the industrial community. This type of robot work in shared environment with humans to perform different tasks safely and efficiently. This causes the robots to be more of companion and human partners than simple machines that do a certain task. This companionship introduces the benefit of human strength in addition to the full benefit of robots. However, performing time/safety critical tasks lead to more challenges.
In collaborative robotics, machines behavior must be clear and understood. The robots should have the ability to interact with each other and efficiently finish their task in shared environment. This can be accomplished by improving the capability of allocating dynamic and partially observed tasks to efficiently finish the job. However, this is a challenge due to the environment variability and uncertainty which introduce large and continuous space that is difficult for the machine to process. Machine Learning (ML) is one area of artificial intelligence that can be applied to develop autonomous policies. Learning and planning ability introduced by ML can also be extended to enable robot to predict others movements and plan its next task.
This research will focus on using ML techniques to improve collaborative robotics interaction in shared environment. The main objective is to improve the efficiency of robots’ interaction in a shared and dynamic environment through continuous planning and prediction. Machine language is one of the approaches that will be considered to answer some of the challenges observed in collaborative robotics, such as uncertain constraint and dynamic multi-agent allocation.