This course is designed to provide students with an in-depth practical experience focusing on one of the most tangible applications of modern AI systems: robot manipulators. We will move beyond classical robotic control methods to explore how contemporary AI techniques, specifically Deep Learning and Reinforcement Learning, are applied to complex robotic tasks. Students are expected to translate theoretical knowledge directly into practical applications by developing algorithms for robot arm task planning, trajectory generation, visual perception, and grasping, either in a simulation or physical robot environment. By the end of the semester, each student or group will deliver a comprehensive final project that successfully accomplishes a chosen robotic task.
Prerequisite(s)
Course Code Course Name…
Corequisite(s)
Course Code Course Name…
Special Requisite(s)
The minimum qualifications that are expected from the students who want to attend the course.(Examples: Foreign language level, attendance, known theoretical pre-qualifications, etc.)
Instructor(s)
Assoc. Prof. Fatma PATLAR AKBULUT
Course Assistant(s)
Schedule
The course has not been opened this semester.
Office Hour(s)
The course has not been opened this semester.
Teaching Methods and Techniques
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Principle Sources
· Robot Modeling and Control - Mark W. Spong, Seth Hutchinson, M. Vidyasagar
· Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
· Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto
Other Sources
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Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction and Robotic Foundations
Oral Presentation, Laboratory
2. Week
Forward and Inverse Kinematics
Oral Presentation, Laboratory
3. Week
Trajectory Planning
Oral Presentation, Laboratory
4. Week
Robot Dynamics and Control
Oral Presentation, Laboratory
5. Week
Sensing and Perception (System Intro)
Oral Presentation, Laboratory
6. Week
Deep Learning for Object Detection
Oral Presentation, Laboratory
7. Week
Deep Learning for Grasping
Oral Presentation, Laboratory
8. Week
MIDTERM
Oral Presentation, Laboratory
9. Week
Reinforcement Learning (RL) Fundamentals
Oral Presentation, Laboratory
10. Week
Model-Based RL Algorithms
Oral Presentation, Laboratory
11. Week
Model-Free RL: Policy Gradient and Actor-Critic
Oral Presentation, Laboratory
12. Week
Sim-to-Real Transfer and Domain Randomization
Oral Presentation, Laboratory
13. Week
Advanced Topics and Ethics
Oral Presentation, Laboratory
14. Week
Project Presenation
Project Presenation
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Understand and implement the forward and inverse kinematics equations of a robot manipulator.
LO-2
Integrate image processing and Deep Learning-based object recognition algorithms (e.g., using CNNs) for robotic tasks.
LO-3
Apply basic Reinforcement Learning (RL) algorithms (e.g., Q-Learning, DQN) to robot arm control within a simulation environment, focusing on minimizing the Loss or Cost function.
LO-4
Decompose a complex robotic task into sub-tasks, develop an end-to-end software architecture (sensing, planning, execution), and submit a formal project report.
LO-5
Effectively use robotic simulation environments (e.g., Gazebo, CoppeliaSim, or PyBullet/MuJoCo) to test and validate their algorithms.
