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Overview
Imagine robots self-organizing into large groups to assist people with physically demanding tasks, leveraging their core capabilities in perception, manipulation, and navigation to interact with the physical world.
To successfully complete a given mission as a team, these robots must make their own decisions to allocate and carry out tasks defined in the mission and cooperate with one another when the task requires it. We realize this capability in multirobot systems through a learning-based paradigm, designing computational models to train a large number of robots to work as a team.
Achieving a high level of autonomy in distributed information processing, decision-making, and collaborative manipulation is crucial. To enable a high level of cooperation among a large number of robots, the project aims to build computational models based on deep reinforcement learning (DRL).
These models are designed to enhance the robots' ability to cooperate in carrying out multiple tasks using their perception, manipulation, and navigation skills. Learn more with this Learning to Cooperate in Multi-robot Task Allocation (Internship) project from King Abdullah University of Science and Technology (KAUST) .
Recommended Student Background
- Robotics
- Machine Learning
- Electrical Engineering
- Computer Science
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Visit programme websiteProgramme Structure
- The main objective of this project is to implement multi-agent DRL in a team of mobile manipulators and validate our DRL implementation through lab experiments.
- Students are expected to collaborate with lab members to explore new ideas and implement them on physical robotic systems. To fulfill the requirements of the project, students should have solid experience working with robotic systems and the Robot Operating System, as well as confidence in C++/Python programming. Experience with reinforcement learning is a plus.
- The model design and experiment reports are expected to be delivered at the end of the internship programme.
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 3 months
Start dates & application deadlines
You can apply for and start this programme anytime.
More details
- Application deadline: all year around
Language
English
PTE Academic 62
IELTS 6.5
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- 4.9/5 student rating
Delivered
On Campus
Campus Location
- Thuwal, Saudi Arabia
Disciplines
Electrical Engineering Computer Sciences View 25 other Short Courses in Electrical Engineering in Saudi ArabiaExplore more key information
Visit programme websiteWhat students do after studying
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Academic requirements
GPA 3.5
English requirements
PTE Academic 62
IELTS 6.5
Prepare for Your English Test
Cathoven AI IELTS Preparation AI-powered IELTS feedback. Clear, actionable, and tailored to boost your writing & speaking score. No credit card or upfront payment required.
- Trusted by 300k learners
- 98 accuracy using real exam data
- 4.9/5 student rating
Other requirements
General requirements
- Undergraduate students in 3rd year or above and master’s students.
- Minimum GPA: 3.5/4
- Curriculum Vitae (CV)
- Statement of purpose
- Contact details of 3 referees
- Official English language test score
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Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at King Abdullah University of Science and Technology (KAUST) and/or in Saudi Arabia, please visit Student Insurance Portal.
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Visit programme websiteTuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
-
International Applies to you
Applies to youNon-residentsFree -
Domestic Applies to you
Applies to youCitizens or residentsFree
Additional Details
- This is a fully-funded internship; it includes tuition, airfare, housing, medical insurance, visa reimbursement and a monthly stipend of $1,000.
Living costs
Thuwal
Saudi Arabia
2270 - 3445 SAR / month
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
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