Master of Science in
Robotics

An accelerated course on modern software design and implementation, which includes modules on abstraction and encapsulation techniques, component-based design, advanced data structures, algorithms, and performance.

Course learning outcomes
1. Explain why concepts such as abstraction and encapsulation are important in modern software design, and demonstrate with real examples.
2. Explain and show how component-based design and object-oriented approaches to software development can be used to enforce good design principles.
3. Perform asymptotic analysis and amortization to demonstrate the efficiency of an algorithm or data structure.
4. Perform rigorous functional and performance evaluations through testing.
5. Describe the key issues relating to the design and implementation of a new programming language.
6. Implement a simple functional or imperative programming language from a language specification.

This course provides a comprehensive treatment of robotic kinematics, with emphasis on the mathematical modeling and analysis of serial and parallel manipulators. Core topics include spatial descriptions and transformations, homogeneous coordinates, Euler angles, and screw-axis representation for describing the position and orientation of rigid bodies. Methods for solving the direct and inverse kinematics of manipulators are presented, using both Denavit-Hartenberg convention and the method of Successive Screw Displacements. The analysis of manipulator workspaces, multiple inverse kinematic solutions, and the classification and position analysis of parallel manipulators are also addressed. Student projects apply these concepts to real robotic systems, typically involving the formulation and solution of the direct and inverse kinematics of industrial manipulators with either open or closed kinematic chains.

Course learning outcomes
1. Formalize the position and orientation of rigid bodies using appropriate spatial representations and transformations.
2. Formalize the direct kinematic model of serial and parallel manipulators.
3. Find the inverse kinematics solutions for open and closed kinematic chains.

The course aims to introduce the students to the state-of-the-art analytical tools and methods used for control system design. The course will start with the modeling of physical systems in the frequency and state space domains. The student will master analytical tools such as Bode plots, root locus diagrams and phase plots. The second part of the course includes frequency domain-based control design (mainly focused on PID controllers) and then covers control system design in state space (linear quadratic regulators, servo-controllers, etc.). In the last part, students will glimpse different advanced control algorithms such as model predictive control, adaptive control and soft-computing-based control. The students will read and analyze advanced literature on the topic regularly.

Course learning outcomes
1. Understand the fundamental concepts of physical system modeling.
2. Apply fundamental frequency and state-space domain techniques in the analysis and design of linear feedback control systems.
3. Have a deep understanding of advanced control algorithms.
4. Implement control algorithms to simulation and real-world systems using software packages, toolboxes and relevant hardware.

This course aims to provide the students with a set of principles and skills that will be of paramount importance for conducting their research and disseminating the corresponding results. The course provides an overview of the scientific method and its application, project planning, working in teams, interaction with sources, and assessment of research results. Students are also taught the fundamental principles necessary for writing technical reports, scientific papers, and research proposals, and for preparing and delivering presentations: the focus of this part is on the connection between the employed research methods and the way in which the results are presented, rather than on the writing/presentation style in itself. Finally, students are exposed to a general introduction on moral reasoning, with subsequent focus on ethics in professional practice and research activity. The latter topic will focus on data management and intellectual property rights, and on the description of phenomena such as plagiarism and data fabrication.

Course learning outcomes
1. Asses a research plan and its rationale, including application of the scientific method, interaction with sources, and critical evaluation of research results.
2. Discuss the principles behind the production of reports, papers, proposals, and scientific presentations.
3. Arguing on ethical principles and their application to professional practice and research.

The course aims to introduce the principles, models and applications of robot vision and perception. Some covered topics are image structure, projection, stereo vision, and the interpretation of visual motion. The practical applicability of the 2-D and 3-D vision algorithms to the robotic problems will also be discussed as case studies such as visual navigation of autonomous robots, robot hand-eye coordination and novel man-machine interfaces.

Course learning outcomes
1. Analyze current research publications about state of the art computer vision techniques.
2. Master the state of the art tools commonly used for developing robot perception applications.
3. Conduct experimental analysis and testing for robot perception using the latest benchmark suites.
4. Apply mathematical and machine learning tools for robot perception.
5. Describe and explain basic principles of audio, visual, tactile data processing with an appreciation for the real-world robot perception issues and constraints.

The course requires students to select a topic, identify authoritative sources, generate an annotated bibliography, and compose a survey paper representing the current state-of-the-art in the selected topic area, and prepare a presentation based on the survey paper. The course includes a series of research presentations, presented by domain experts, to expose students to current research programs, and facilitate the thesis topic selection by students.

Course learning outcomes
1. Conduct a literature search.
2. Prepare a detailed bibliography.
3. Analyze current research and critically review for strengths and weaknesses.
4. Recognize and extract relevant information from scientific papers, seminars, and presentations.
5. Prepare a survey paper representing the state-of-the art in the topic area.
6. Present the results to both technical and non-technical audiences.

This course introduces the students to best-practice pedagogical methods and innovations in teaching and learning, under the mentorship of senior faculty. The students will conduct classroom and laboratory observations of prominent instructors, using a variety of teaching and learning styles. Later, they will apply educational, instructional, and assessment methodologies in recitation sections of their respective disciplines under the supervision of an experienced faculty member. They will summarize their observations and experiences in weekly activity logs and a final summative report.

Course learning outcomes
1. Recognize high-level program and curricular structure & purpose.
2. Identify and describe a variety of pedagogical methods.
3. Specify specific learning objectives and outcomes for a given topic.
4. Utilize Bloom’s Taxonomy in the articulation of learning outcomes.
5. Select appropriate assessment methodologies suitable to determine student achievement of stated learning outcomes.
6. Identify indicators of potential academic misconduct, and appropriate responses.
7. Conduct the reflective exercises necessary to implement ongoing quality enhancement processes.

Students will apply educational and instructional methodologies in laboratory and practical sessions of their respective disciplines under the supervision of an experienced faculty member. They will summarize their observations and experience in a final report.

Course learning outcomes
1. Identify pedagogical methods for laboratory sessions.
2. Select appropriate assessment methodologies for evaluate the level of achievement of learning outcomes of the specific lab activity.
3. Conduct reflective exercises to assess the effectiveness of the laboratory learning experience, and implement modifications aimed at quality enhancement.

This course empowers students to develop and implement rigorous research methodologies, from formulating research hypotheses to analyzing and interpreting results. Students will gain hands-on experience in conducting simulations or practical experiments, and they will learn to effectively communicate their findings through the preparation and defense of a comprehensive thesis.

Course learning outcomes
1. Conduct thorough literature reviews to support research proposals.
2. Formulate clear and testable research hypotheses.
3. Develop and implement appropriate research methodologies.
4. Independently conduct research experiments and simulations.
5. Effectively communicate research findings and defend research projects.

This course equips students with advanced research skills, enabling them to independently plan and conduct research projects. Students will learn to critically analyze research findings and effectively communicate their results through presentations and written reports. The course culminates in the development of a comprehensive thesis that demonstrates mastery of research methodologies and critical thinking.

Course learning outcomes
1. Independently conduct advanced-level research projects.
2. Critically analyze research findings and adhere to ethical guidelines.
3. Effectively communicate research results to diverse audiences.
4. Produce high-quality research theses.

After introducing the definition and applications of robot motion planning, the course provides a basic treatment of different algorithms, mainly focusing on offline planning. Methods including search-based planning, virtual potential fields, visibility maps, and sampling-based approaches are covered. The course then focuses on numerical optimal control and dynamic programming for robot motion planning. Approximate solutions to dynamic programming problems are also considered, along with their relationship to deep reinforcement learning. Finally, the course focuses on model predictive control for online motion planning problems. In addition to a general treatment of the theory underlying the described algorithms, the course discusses simple applications involving mobile robots and robot manipulators.

Course learning outcomes
1. Categorize and discuss motion planning algorithms for simple applications.
2. Assess the characteristics of algorithms based on dynamic programming.
3. Illustrate the fundamental properties of numerical optimal control and model predictive control algorithms.

The course introduces fundamental hardware and software concepts of computer systems as core elements of modern intelligent systems. Topics covered include basic microprocessor architectures, memory allocation, concurrency principles (multitasking, process synchronization), real-time systems, embedded and mechatronic system design. Course project assignments are related to the theoretical material covered in lectures with focus on developing practical skills of embedded system and GUI design using C/C++ programming languages in an embedded Linux operating system.

Course learning outcomes
1. Apply principles of embedded computing, hardware interfacing, and concurrent programming in computer-controlled technical systems.
2. Develop sensor and actuator interfaces on an embedded computing platform using appropriate communication, filtering, control and programming techniques.
3. Integrate sensing, computation, actuation, and control into a functional technical system prototype.
4. Communicate design concepts, technical decisions, experimental results, and system performance effectively in written reports and demonstrations.
5. Work effectively in teams to plan, develop, and deliver a complete engineering project.

The course introduces modern principles of robot manipulation, including classical and learning-based robot control, motion planning and object grasping approaches. Topics include robot differential/inverse differential kinematics, optimization-based motion planning, manipulator statics and dynamics, joint and Cartesian position control, force and impedance control. Course project assignments focus on the implementation of robot control approaches and developing a complete object picking-and-placing software stack for a commercial robot-manipulator using state-of-the-art Robot Operating System 2 (ROS 2).

Course learning outcomes
1. Explain the core principles of intelligent robot manipulation, including motion planning, grasping, and control.
2. Analyze and present state-of-the-art research literature on learning-based robot manipulation.
3. Apply classical and learning-based approaches to robot manipulation control.
4. Develop ROS 2-based software solutions for motion planning and object manipulation.
5. Work effectively in a team to design, implement and evaluate a robot manipulation project.

Brain-Machine Interface (BMI) systems is an emerging interdisciplinary field at the intersection of engineering, neuroscience and medicine. It has brought promising new perspectives to human-machine interaction using brain activity. It provides users the capability to directly control applications such as assistive robotic technologies. This course is an introduction to the fundamentals in BMI technology and discusses its applications including both invasive and non-invasive BMI systems for controlling user interfaces, prosthetic arms, wheelchairs, and robotic exoskeletons. The course will also introduce other clinical applications of BMI technology for patients with locked-in syndrome and its utility in restoring movement and mobility in severely paralyzed persons. In addition, other non-clinical use of BMI technology will be studied with hands-on experiments/projects for applications such as security, alertness monitoring, entertainment, gaming and education or human augmentation. The course will be delivered in the form of mixed lectures with case studies and discussion of research papers led by student groups, and in-class demonstration of BMI systems using the systems available at NU. The lecture series will cover core topics related to neural signal processing and machine learning algorithms that convert EEG features into control commands (i.e. an upper-limb rehabilitative robot).

Course learning outcomes
1. Demonstrate advanced knowledge of BMI principles, including neural signal acquisition, feature extraction, and decoding strategies.
2. Apply and evaluate machine learning algorithms for neural signal classification and prediction tasks in BMI systems.
3. Design and implement BMI systems using modern tools and programming environments such as Python and MATLAB.
4. Critically analyze BMI research literature and synthesize insights to support innovative problem-solving.
5. Develop novel BMI applications addressing challenges in healthcare, assistive technologies, and robotics.
6. Present technical findings and research outcomes effectively through written and oral communication.

This course aims to provide state-of-the-art concepts in optimal control, with applications to robot motion planning. First, a brief introduction to optimization is provided. Then, the course focuses on optimal control algorithms for deployment in practical robotics and mechatronics. Later, disturbance observers will be introduced to the dynamic systems with disturbance. The course covers linear-quadratic regulators and disturbance-observer-based control systems.

Course learning outcomes
1. Design linear optimal control algorithms
2. Apply the methods to reference tracking problems in robotics and mechatronics applications.
3. Design linear disturbance observer and incorporate it to controller to improve the tracking performance