Get a Collaborative Specialization in Robotics!

The Collaborative Specialization is for research stream graduate students pursuing a robotics thesis with a UofT Robotics Institute faculty member.

Who it’s for: are you eligible?

Recognition: transcript notation

Why pursue a Collaborative Specialization in Robotics at UofT?

Robotics is a fast-changing, highly interdisciplinary field. U of T’s Collaborative Specialization in Robotics brings together research stream graduate students from engineering, computer science and medicine who are advancing the state of the art in robotics across healthcare, mobility, and advanced manufacturing applications.

The program complements a student’s research thesis and coursework with a seminar-style course, events, and professional development skills, and provides all research-stream graduate students with a robotics designation on their degree.

Who it’s for

The Collaborative Specialization in Robotics is open to research-stream graduate trainees. You must meet all of the below criteria to be eligible for the Collaborative Specialization in Robotics.

Eligibility criteria for the Collaborative Specialization in Robotics:

✅ You are already admitted to a participating research-stream master’s degree program or PhD program at U of T. Participating degree programs include:

Aerospace Science and Engineering — MASc, PhD
Biomedical Engineering — MASc, PhD
Computer Science — MSc, PhD
Electrical and Computer Engineering — MASc, PhD
Mechanical and Industrial Engineering — MASc, PhD
Rehabilitation Science — MSc, PhD

✅ You are supervised by a Robotics Institute faculty member or a faculty member of a participating degree program.

✅ You are undertaking a thesis where the robotics connection is clear and tangible.

✅ You have registered your intent to pursue the Collaborative Specialization.

You are NOT eligible to participate in the Collaborative Specialization in Robotics if:

You are not enrolled in one of the participating degree programs listed above.
You have already completed U of T’s Collaborative Specialization in Robotics at the master’s level and are currently enrolled in a PhD program.

Not yet a student at U of T?

You must already be enrolled in a MASc or MEng or PhD program at one of our qualifying Departments or Institutes at U of T in order to be eligible for the Graduate Emphasis in Robotics.

If you are not yet a graduate student at U of T, check out the admissions pages of our partner departments!

Currently doing your MEng at U of T?

If you are doing your MEng you are not eligible to participate in the Collaborative Specialization.

The good news is that you are eligible for the Graduate Emphasis in Robotics! Don’t forget to apply before you graduate!

Note for research-stream students:

If you started your degree prior to Fall 2022, you can still pursue the Emphasis in Robotics. You also may be able to complete the Collaborative Specialization instead if you meet the requirements. If you have questions, please contact robotics.gradstudies@utoronto.ca for more information.

How to Complete the Collaborative Specialization

Register your intent to pursue the Collaborative Specialization (you need to login with your UTORid to access)
Once your form is processed, you’ll be registered for the seminar course ROB1830Y on Acorn by either Sept 10, Jan 10, or May 1st.
Keep a record of your seminar attendance throughout your degree by capturing:
1. Name of seminar speaker, title of talk
2. Date you participated or watched the seminar. Note: Your seminar attendance will be verified. you can remain registered in the course across multiple years until you meet the course requirements (i.e. you need not take eight seminars in one academic year).

When you have met the requirements of the collaborative specialization (ie.completed your thesis and the seminar course requirements), fill out the Collaborative Specialization Completion Form.

Program Requirements

Complete your thesis in the area of robotics
Complete one half course (0.5 FCE) from the following list.
Complete eight seminars in ROB1830Y, the Robotics Seminar Series (0.0 FCE, Credit/No Credit) in addition to your home program requirements. ROB1830Y is a “continuous course”, meaning that you can remain registered in the course across multiple years until you meet the course requirements (i.e. you need not take eight seminars in one academic year).
Meet the degree requirements of the School of Graduate Studies, and your participating home graduate program.

Group 1: Planning and Control

AER 1516H - Motion Planning for Robotics

A rigorous mathematical study of the motion planning problem for aerial, ground, and mobile manipulator robot platforms and for multi-robot systems. Geometric representations and the robot configuration space. Sampling-based motion planning. Feedback motion planning in continuous spaces. Planning under sensor uncertainty and with differential constraints.Course project involving the implementation of modern planning algorithms in simulation and (potentially) on a real mobile manipulator.

AER 1517H - Control for Robotics

This course presents optimal, adaptive and learning control principles from the perspective of robotics applications. Working from the Hamilton-Jacobi-Bellman formulation, optimal control methods for aerial and ground robots are developed. Real world challenges such as disturbances, state estimation errors and model errors are addressed and adaptive and reinforcement learning approaches are derived to address these challenges. Course project involves simulated control of an aerial vehicle, with aerodynamic models and wind disturbances.

ECE 557H - Linear Control Theory (Exclusion: ECE410H)

ECE 1619H - Linear Geometric Control Theory

The course presents a more advanced treatment of linear control theory via the geometric approach. The coverage roughly corresponds to the first six chapters of “Linear Multivariable Control: A Geometric Approach”, by W.M. Wonham. We adopt the abstract algebra approach of the text to study controllability, observability, controlled invariant subspaces, controllability subspaces, and controllability indices. These concepts are applied to solve the problems of stabilization, output stabilization, disturbance decoupling, and the restricted regulator problem. Areas of current research in linear geometric control will also be discussed.

Prerequisite: ECE410H1 or ECE557H1 or an equivalent State Space Control course.

ECE 1658H - Geometric Nonlinear Control of Robotic Systems

This course presents recent developments on control of underactuated robots, focusing on the notion of virtual constraint. Traditionally, motion control problems in robotics are partitioned in two parts: motion planning and trajectory tracking. The motion planning algorithm converts the motion specification into reference signals for the robot joints. The trajectory tracker uses feedback control to make the robot joints track the reference signals. There is an emerging consensus in the academic community that this approach is inadequate for sophisticated motion control problems, in that reference signals impose a timing on the control loop which is unnatural and inherently non robust. The virtual constraint technique does not rely on any reference signal, and does not impose any timing in the feedback loop. Motions are characterized implicitly through constraints that are enforced via feedback. Through judicious choice of the constraints, one may induce motions that are surprisingly natural and biologically plausible. For this reason, the virtual constraints technique has become a dominant paradigm in bipedal robot locomotion, and has the potential of becoming even more widespread in other area of robot locomotion. The virtual constraint approach is geometric in nature. This course presents the required mathematical tools from differential geometry and surveys the basic results in this emergent research area.

– Virtual constraint generators
– Stabilization of periodic orbits on the constraint manifold.
– Virtual constraints for walking robots

Required backgound: This course has no formal prerequisites, but assumes knowledge of vector calculus and linear algebra. Ideally, the student taking this course will have taken an introductory course on nonlinear control theory, such as ECE1647F, and be familiar with the Lagrangian modelling of robots from a course like ECE470.

Prerequisites: ECE410 or ECE557 or equivalent.

ECE 1636H - Control of Discrete-Event Systems I

This course is an introduction to the control of discrete, asynchronous, nondeterministic systems like manufacturing systems, traffic systems, and certain communication systems. Architectural issues (modular, decentralized and hierarchical control) are emphasized. The theory is developed in an elementary framework of automata and formal languages, and is supported by a software package for creating applications. There are no special prerequisites.

ECE 1647H - Introduction to Nonlinear Control Systems

This course is a mathematical introduction to nonlinear control theory, a subject with roots in dynamical systems theory, mechanics, and differential geometry. The focus of this course is on the dynamical systems perspective. The material covered in this course finds application in fields as diverse as orbital mechanics and aerospace engineering, circuit theory, power systems, robotics, and mathematical biology, to name a few. The course is organized in four chapters, as follows.

Vector Fields and Dynamical Systems: Finite dimensional dynamical systems, vector fields, and their equivalence. Existence and uniqueness of solutions of ODEs.
Foundations of Dynamical Systems Theory: Invariant sets and their characterization by the Nagumo theorem. Limit sets as a tool to characterize the asymptotic behaviour of bounded orbits. Limit sets of two-dimensional systems: the Poincaré-Bendixson theorem. Poincaré theory of stability of closed orbits. Linearization of vector fields about equilibria. Linearization of vector fields about closed orbits.
Foundations of Stability Theory: Equilibrium stability and its characterization by means of Lyapunov’s theorem. Domain of attraction of an equilibrium. The Krasovskii-LaSalle invariance principle. Stability of LTI systems, and exponential stability of equilibria. Converse stability theorems.
Introduction to Nonlinear Stabilization: Control-Lyapunov functions. Parametrization of equilibrium stabilizers by CLFs (Artstein-Sontag theorem). Passive systems and passivity-based equilibrium stabilization. Passivity of mechanical control systems. Port-Hamiltonian systems.

ECE 1653H - Hybrid Systems and Control Applications

The course explores design of control systems that achieve complex specifications. This is an emerging area in control theory that contrasts with traditional control design focused on stabilization and tracking. We introduce linear temporal logic (LTL) and show how LTL specifications capture a rich class of transient and steady-state behaviours of control systems. The LTL control problem is reduced to a hybrid control problem using ideas from computer science to obtain a design that includes high-level discrete algorithms and low-level continuous time controllers. The course covers the most important techniques and tools that come to play in this methodology, including triangulation, behaviour of affine systems on polytopes, the Reach Control Problem (RCP), and flow functions. We explore in depth control synthesis methods for the RCP based on affine, continuous state, and piecewise affine feedbacks. The techniques studied in the course come together to solve the problem of motion planning for a group of quadrocopters.
Prerequisites: ECE410 or ECE557 or equivalent.

ECE 1657H - Game Theory and Evolutionary Games

This course presents a mathematical treatment of classical and evolutionary game theory. Topics covered in classical game theory: matrix games, continuous games, Nash equilibrium (NE) solution, existence and uniqueness, best-response correspondence. Topics covered in evolutionary games: evolutionary stable strategy concept, population games, replicator dynamics, relation to dynamic asymptotic stability. Learning in games: imitation dynamics, fictitious play and their relation to replicator dynamics. Applications to engineering: communication networks, multi-agent learning. There is no required textbook. PDF course notes are available; the notes are self-contained and serve as a textbook. Weekly formal lectures based on the course notes.

MIE 1064H - Control Methods With Applications To Robotics

The main purpose of this course is to introduce a series of distinct topics in control to students who have not seen control system design beyond a first course in control, which includes classical methods such as root locus, and Bode design, for example. The topics discussed in MIE 1064 F are selected to give students a broad overview of a variety of control design methods and concepts in stability.

Prerequisite: At least one introductory course in control is required and a Mechanical Engineering course in one of either Mechanisms or Vibrations.

MIE 1068H - Nonlinear Control

This course provides an introduction to the main concepts in nonlinear control system design. The
emphasis in this class is on issues encountered in application to physical systems. The first portion of this course reviews stability analysis tools for nonlinear systems. The second portion of this class focuses on control design using methods such as feedback linearization, sliding mode control, and adaptive control. The last portion of the class looks at practical implementation for applications of interest such as robotics, flight control, and process control.

ECE 1635 - Special Topics in Control I

ECE 1505 - Convex Optimization

This course provides a comprehensive coverage of the theoretical foundation and numerical algorithms for convex optimization with engineering applications. Topics include: convex sets and convex functions; convex optimization problems; least-square problems; optimal control problems; Lagrangian duality theory. Karush-Kuhn-Tucker (KKT) theorem; Slater’s condition; generalized inequalities; minimiax optimization and saddle point; introduction to linear programming, quadratic programming, semidefinite programming and geometric programming; numerical algorithms: descent methods, Newton’s method, interior-point method; convex relaxation; applications to communications and signal processing.

Group 2: Perception and Learning

AER 1513H State Estimation for Aerospace Vehicles

This course introduces the fundamentals of state estimation for aerospace vehicles. Knowing the state (e.g., position, orientation, velocity) of a vehicle is a basic problem faced by both manned and autonomous systems. State estimation is relevant to aircraft, satellites, rockets, landers, and rovers. This course teaches some of the classic techniques used in estimation including least squares and Kalman filtering. It also examines some cutting edge techniques for nonlinear systems including unscented Kalman filtering and particle filtering. Emphasis is placed on the ability to carry out state estimation for vehicles in three- dimensional space, which is complicated by vehicle attitude and often handled incorrectly. Students will have a chance to work with datasets from real sensors in assignments and will apply the principles of the course to a project of their choosing.

AER 1515H Perception for Robotics (Exclusion: ROB1514)

This course presents the fundamentals of robotic perception based on a foundation of probability, statistics and information theory. Common sensor types and their probabilistic modeling are surveyed, including computer vision, Lidar, radar, GNSS/INS and odometry. Methods for feature extraction, description & matching, direct photometric and point cloud registration, outlier rejection are presented in the context of a robotic localization and mapping front end. Object detection and tracking, semantic segmentation and prior maps are fused to form a complete perceptual view of dynamic environments for a wide range of robotic applications.

CSC 2503H - Foundations of Computer Vision

This course offers an in-depth, graduate-level introduction to computer vision. Topics covered include the following: (1) Camera system geometry, geometric transformations, multi-view geometry, projective and metric reconstructions. (2) Image acquisition, scene lighting and reflectance models. (3) The robust estimation of edges, lines, and regions. (4) Image matching and the estimation of motion in image sequences. (5) Advanced topics in visual inference, including Markov random fields and deep learning for computer vision.

CSC 2506H - Probabilistic Reasoning

An introduction to probability as a means of representing and reasoning with uncertain knowledge, with an emphasis on graphical probability models. Topics covered will include: the formalism of probability and its interpretations, qualitative specification of probability distributions by means of independence relationships expressed using graphical models, quantitative specification of probability distributions parameterized using graphical models, algorithms for probabilistic reasoning with graphical models, elicitation of probability models from experts, learning probability models from empirical data, inferring causal relationships, alternative, non-probabilistic formalisms for expressing uncertain or imprecise knowledge.

CSC 2515H - Introduction to Machine Learning

Machine learning is a set of techniques that allow machines to learn from data and experience, rather than requiring humans to specify the desired behavior by hand. Over the past two decades, machine learning techniques have become increasingly central both in AI as an academic field, and in the technology industry. This course provides a broad introduction to some of the most commonly used ML algorithms. The first half of the course focuses on supervised learning. We begin with nearest neighbours, decision trees, and ensembles. Then we introduce parametric models, including linear regression, logistic and softmax regression, and neural networks. We then move on to unsupervised learning, focusing in particular on probabilistic models, but also principal components analysis and K-means. Finally, we cover the basics of reinforcement learning.

CSC 2541H - Topics in Machine Learning

CSC 2548H - Machine Learning in Computer Vision

This class is a graduate seminar course in computer vision. The class will cover a diverse set of topics in Computer Vision and various machine learning approaches. It will be an interactive course where we will discuss interesting topics on demand and latest research buzz. The goal of the class is to learn about different domains of vision, understand, identify and analyze the main challenges, what works and what doesn't, as well as to identify interesting new directions for future research.

Prerequisites: Courses in computer vision and/or machine learning (e.g., CSC320, CSC420, CSC411) are highly recommended (otherwise you will need some additional reading), and basic programming skills are required for projects.

ECE 516H - Intelligent Image Processing

This course provides the student with the fundamental knowledge needed in the rapidly growing field of Personal Cybernetics, including “Wearable Computing”, “Personal Technologies”, “Human Computer Interaction (HCI)," "Mobile Multimedia," "Augmented Reality," "Mediated Reality," CyborgLogging," and the merging of communications devices such as portable telephones with computational and imaging devices. The focus is on fundamental aspects and new inventions for human-computer interaction. Topics to be covered include: mediated reality, Personal Safety Devices, lifelong personal video capture, the Eye Tap principle, collinearity criterion, comparametric equations, photoquantigraphic imaging, lightvector spaces, anti-homomorphic imaging, application of personal imaging to the visual arts, and algebraic projective geometry.

ECE 1511H - Signal Processing

Signal processing techniques using special purpose digital hardware and general purpose digital computers are playing an increasingly important role. The course deals with some introductory and some advanced topics in the area. In particular, it presents the characterization of random discrete time signals. It provides an introduction to traditional and modern statistical discrete time signal processing frameworks, including processing with second-, higher- and fractional lower -order statistics. It discusses sampling and multirate signal conversion; linear prediction and optimum linear filters; least squares methods for system modeling and design; theory and applications of adaptive filters. It also deals with applications in signal and image processing and analysis.
Prerequisites: ECE310H1, ECE431H1, ECE302H1 or equivalent.

ECE 1512H - Digital Image Processing and Applications

This course will present the concepts of the main processing techniques for digital image processing. It will cover image enhancement and restoration, digital filtering (linear and nonlinear), local space operators, image analysis, and elements of vision. It will also describe the impact of digital image processing to the more important fields of application.
Prerequisites: ECE431H1 or equivalent.

JEB 1433H - Medical Imaging

We will present an elementary introduction to the revolutionary and important new theory of Compressed Sensing. We will fill in the basic mathematical prerequisites on Fourier Transforms and Wavelets. Other topics will depend on the interests of the class: we will choose between a detailed explanation of how MRI works, imaging electric properties of tissue, or present modern techniques in signal processing for denoising, segmentation and registration.

ROB 501H - Computer Vision for Robotics

STA 2104 - Statistical Methods for Machine Learning II

The language of probability allows us to coherently and automatically account for uncertainty. This course will teach you how to build, fit, and do inference in probabilistic models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. This course will teach the basic building blocks of these models and the computational tools needed to use them.

ECE 1513 - Intro to Machine Learning (cannot substitute with CSC 2515)

An Introduction to the basic theory, the fundamental algorithms, and the computational toolboxes of machine learning. The focus is on a balanced treatment of the practical and theoretical approaches, along with hands on experience with relevant software packages. Supervised learning methods covered in the course will include: the study of linear models for classification and regression and neural networks. Unsupervised learning methods covered in the course will include: principal component analysis, k-means clustering, and Gaussian mixture models. Techniques to control overfitting, including regularization and validation, will be covered.

CSC 2606 - Continuum Robotics

Continuum robots differ fundamentally from traditional robots, as they are jointless structures. Their appearance is evocative of animals and organs such as trunks, tongues, worms, and snakes. Composed of flexible, elastic, or soft materials, continuum robots can perform complex bending motions and appear with curvilinear shapes. Continuum robots have a high potential to navigate and operate in confined spaces currently unreachable to standard robots, as their diameter to length ratio can be as low as 1:300. Typical applications are in minimally invasive surgery or in maintenance, repair and operation. This introductory course covers the fundamentals of continuum robot design, modelling, planning, and control. Students will code their own continuum robot simulator.

MIE 1077 - Artificial Intelligence for Robotics III (AIR III): Advanced AI-Based Robot Applications

ECE 1508H - Special Topics in Communications: Applied Deep Learning

Deep learning has become an integrated part of many engineering designs, and is a fundamental component of many recent fields, such as Natural Language Processing and Computer Vision. This course gives a comprehensive introduction to deep learning by going through various neural network architectures in detail, exploring their applications and learning their implementation.

The main part of the course goes through the fundamental neural network architectures, namely Feedforward Neural Networks (FNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). For each architecture, the course explains (1) the type of problems that can be solved by the architecture, (2) its training and hyperparameter tuning, (3) its efficient implementation, (4) the challenges that are faced in its implementation, and (5) the state-of-the-art techniques to overcome the implementational challenges. The course further introduces the well-known deep learning models used frequently in practice and goes through some selected advanced topics, such as Sequence-to-Sequence Models and Auto-Encoders. Most of the learning in the course is acquired through detailed programming assignments and a final project, where the students get the chance to implement and test their own deep learning model for an applied problem.

ECE 1508H - Special Topics in Communications: Reinforcement Learning

Prerequisites: ECE1508H “Special Topics in Communications: Applied Deep Learning” / ECE1513H / ECE421H1 / CSC311H1 / CSC413H1 / APS360H1 or equivalent course on Machine Learning, with Neural Networks

Exclusions: CSC2547H "Topics in Machine Learning: Reinforcement Learning"

This course provides a concrete understanding of reinforcement learning and its applications. The ultimate goal of the course is to develop hands-on skills in deep reinforcement learning, for which fundamentals of reinforcement learning are first discussed and then deep reinforcement learning algorithms are studied. The course is designed in three major parts: the first part gives the students a warm welcome by taking them through the basic definitions and fundamental concepts. The second part explains fundamental reinforcement learning methods by touching the key model-based and model-free techniques and providing deep understanding of these methods. The last part explores deep reinforcement learning, where deep neural networks are employed to efficiently approximate the developed techniques in Part 2. In this part, we take a look into several topics and algorithms, such as function approximation, deep Q-learning, policy gradient methods and proximal policy optimization algorithms.

The first two parts of the lecture can be readily followed and only require basic knowledge in Linear Algebra and Probability. The last part needs an understanding of concepts in machine learning, particularly the concept of supervised learning and neural networks. A primary course in Machine Learning is a prerequisite for this course. Hands-on and analytic skills are developed in this course through three sets of assignments and a final project. These items extensively deal with programming in Python. Basic knowledge in Python programming is hence required for this course.

Group 3: Modelling and Dynamics

AER 506H - Spacecraft Dynamics and Control I

Rigid body kinematics and dynamics. Orbital dynamics and control: the two-body problem, orbital perturbations, orbital maneuvers, interplanetary trajectories, the restricted three-body problem. Attitude dynamics and control: torque-free motion, spin stabilization, dual-spin stabilization, disturbance torques, gravity-gradient stabilization, active spacecraft attitude control, bias-momentum stabilization.

AER 1503H - Spacecraft Dynamics and Control II

Advanced topics in spacecraft dynamics and control. Course includes a project. Topics include input-output stability analysis and Lyapunov stability analysis with applications to spacecraft attitude control; feedforward, feedback, and adaptive controller design. Quaternion feedback. Linear state-space analysis and observer-based compensator design. Flexible spacecraft dynamics: equations of motion, spatial discretization, modal equations, constrained and unconstrained modes. Flexible spacecraft control: spillover, controller discretization, LQG, H-infinity, and positive real design.

AER 1512H - Multibody Dynamics

This is a seminar course designed to introduce students to the fundamentals of multibody dynamics with particular emphasis on the dynamics of robotic systems. Each student, in consultation with the course coordinator, will be required to select two topics in the area, investigate them thoroughly and present a seminar on each to the other members of the class. Students may choose topics well-treated in the mechanical literature or ones which are more research-oriented, perhaps requiring some original input on the part of the student.

MIE 1001H - Advanced Dynamics

MIE 1005H - Theory of Vibration

JEB 1444H - Neural Engineering

CSC2626 - Imitation Learning for Robotics

This graduate-level seminar course will examine some of the most important papers in imitation
learning for robot control, placing more emphasis on developments in the last 10 years. Its purpose is to
familiarize students with the frontiers of this research area, to help them identify open problems, and to
enable them to make a novel contribution. The majority of lectures, particularly after the first two weeks
of introductory material, will consist of in-class student presentations. This course will broadly cover the
following areas:
• Imitating the policies of demonstrators (people, expensive algorithms, optimal controllers)
• Connections between imitation learning, optimal control, and reinforcement learning
• Learning the cost functions that best explain a set of demonstrations
• Shared autonomy between humans and robots for real-time control
The course involves a significant final project component, which will likely involve the use of robot simulators.
Students who are interested in using real robot hardware and have shown sufficien

Group 4: Systems Design and Integration

AER 525H - Robotics (Exclusion: ECE470H)

This course extends the fundamentals of analytical robotics to design and control of industrial and aerospace robots and their instrumentation. Topics include forward, inverse, and differential kinematics, screw representation, statics, inverse and forward dynamics, motion and force control of robot manipulators, actuation schemes, task-based and workspace design, position and force sensors, tactile sensing, and vision and image processing in robotic systems. Course instruction benefits from the courseware technology that involves a Java-based on-line simulation and other multimedia means for presenting realistic demonstrations and case studies in the context of teaching advanced notions. A series of experiments in the Robotics Laboratory will also enhance the practical notions of the course content.

AER 1216H - Fundamentals of UAVs

Unpiloted aircraft, known as UAVs, drones or aerial robots, are very quickly becoming a major sector of the aerospace industry. They are increasingly used in aerial photography, inspection of infrastructure, delivery of small packages and other applications requiring inexpensive and flexible flight. The basic physical, scientific and engineering principles necessary to design a remote-controlled fixed-wing or quad-rotor UAV are explained in this course. These include aerodynamics, propulsion, structures and control. A key part of this course will be a group project to create a detailed design of a UAV that is capable of performing a specific function.

AER 1217H - Development of Autonomous UAVs

This course is the second part of the CARRE core courses, following AER1216: Fundamentals of UAVs, which covers the fundamental principles related to UAV design: structures, aerodynamics and control. AER1216 is the prerequisite of this course, unless approved by the instructor. In AER 1217, the focus is placed on the development of unmanned aerial systems (UAS), with the theme of autonomy in navigation and control, as well as flight performance analysis and evaluation.

The course curriculum will be delivered in both lectures and development projects, including flight tests. The contents include: quadrotor or fixed-wing UAV dynamics and control; sensing and estimation for UAVs; navigation and path planning; instrumentation and sensor payloads; computer vision. A development project will be given to students who will use the UAV platform to design an autonomous system to accomplish a specific flying mission, to be demonstrated by flight experiments.

Prerequisite:

AER 1216H “Fundamentals of UAVs” or equivalent with permission of the instructor

CSC 2621H - Topics in Robotics

ECE 470H - Robot Modeling and Control (Exclusion: AER525H)

MIE 1070H - Intelligent Robots for Society

This course introduces the design of intelligent robots- focusing on the principles and algorithms needed for robots to function in real world environments with people. Topics that will be covered include autonomy, social and rational intelligence, multi-modal sensing, biologically inspired and anthropomorphic robots, and human-robot interaction. Class discussions will centre on the interactive, personal assistive and service robotics fields.
Prerequisites: MIE404 AND MIE444, or equivalent courses. Please note that the course builds on already existing knowledge of feedback control theory and mechatronics systems. Students taking this course should already be adept in these topics as they will not be covered again here.

MIE 1071H - Advanced Robotics

MIE 1075H - AI Applications in Robotics

MIE 1809 - Advanced Mechatronics

MIE 505H - Micro/Nano Robotics

This course will cover the design, modeling, fabrication, and control of miniature robot and micro/nano-manipulation systems for graduate and upper level undergraduate students. Micro and Nano robotics is an interdisciplinary field which draws on aspects of microfabrication, robotics, medicine and materials science. In addition to basic background material, the course includes case studies of current micro/nano-systems, challenges and future trends, and potential applications. The course will focus on a team design project involving novel theoretical and/or experimental concepts for micro/nanorobotic systems with a team of students. Throughout the course, discussions and lab tours will be organized on selected topics.

MIE 506H - MEMS Design and Microfabrication

This course will present the fundamental basis of microelectromechanical systems (MEMS). Topics will include: micromachining/microfabrication techniques, micro sensing and actuation principles and design, MEMS modeling and simulation, and device characterization and packaging. Students will be required to complete a MEMS design term project, including design modeling, simulation, microfabrication process design, and photolithographic mask layout. Prerequisite: MIE222H1, MIE342H1

ROB 521H - Mobile Robotics and Perception

The course addresses fundamentals of mobile robotics and sensorbased perception for applications such as space exploration, search and rescue, mining, self-driving cars, unmanned aerial vehicles, autonomous underwater vehicles, etc. Topics include sensors and their principles, state estimation, computer vision, control architectures, localization, mapping, planning, path tracking, and software frameworks. Laboratories will be conducted using both simulations and hardware kits.
Prerequisite: ROB310H1, AER372H1

ROB 1514H - Introduction to Mobile Robotics

This course introduces the basic of controlling mobile robots, with emphasis on techniques for use outdoors. Mobile robots have found application in space exploration (e.g., Mars Exploration Rovers), mining, bomb disposal, search and rescue, and vacuuming our homes. The future will see mobile robotics technology paving the way to such things as home assistants and automated roadways. This course will present the current state of the art in mobile robotics in terms of sensing and algorithms. Concepts will be learned through experimentation with a mobile robotics kit.

Topics include: introduction to mobile robotics, review of probability theory, sensors, computer vision, simultaneous localization and mapping (SLAM), place recognition, terrain assessment, path planning, path tracking, experimental testing. It is not recommended to take both AER 521 and AER 1514.

Note that this course will be superseded by the courses AER 1515H, AER 1516H and AER 1517H. ROB 1514H will be discontinued after the 2019-2020 academic year.

Exclusion: AER 1515H

ROB1830Y - Collaborative Specialization in Robotics Seminar Series

This is a seminar-style graduate course for students who are registered in the Collaborative Specialization in Robotics. Students will attend at least eight talks from a wide variety of robotics experts from research and industry in order to learn about areas of robotics outside their thesis research. The course is “continuous”, meaning that students can remain registered in the course across multiple years until they meet the course requirements (i.e. students need not take eight seminars in one academic year). All seminars will be delivered either online or in hybrid delivery mode to allow attendance by students on any campus.

Prerequisites: Students must be enrolled in the Collaborative Specialization in Robotics in order to register for this course.

MIE 1076 - AI Applications in Robotics II

MIE 1080 - Introduction to Healthcare Robotics

This course provides students with knowledge on healthcare robotics including surgical, assistive, and rehabilitation robots, plus essential skills in ethics, design, IP and market considerations. Specific topics include medical imaging-guided surgery; minimally-invasive surgery through miniaturization, novel actuation and sensing; robotic surgery at tissue and cell levels; autonomous robotic systems to assist with daily living activities; multi-modal robot interfaces; robotics-based rehabilitation technologies; upper limb rehabilitation robots; wearable exoskeletons and sensors; implanted neural interfaces. Students are provided with state-of-the-art advances in healthcare robotics.

MIE 1050 - Design of Intelligent Sensor Networks

This course will provide students with practical knowledge on sensor network design including sensor selection, calibration, digitization, and digital signal processing. Students will be introduced to theory and operation of various sensor technologies and their applications. Commonly used transducers such as chemical, mechanical, and magnetic as well as the more advanced organic and nuclear transducers are discussed. This course will also cover linear and non-linear multi-parameter calibration. Digitization, and a survey of digital signal processing techniques will be discussed with practical application of commonly used digital filters. Special focus will be placed on optimal design of sensor networks and multi-sensor data fusion. There will be a design project to enforce the lessons learned in class on sensor calibration and digital signal processing.

Not all courses will be offered each year.

Deadlines

Declare your intent to pursue the Collaborative Specialization in Robotics. Forms are processed three times a year, and the deadlines are: Sept 5, Jan 5, April 25. Please review the eligibility requirements for the Collaborative Specialization before submitting the declaration form to be sure you are eligible to enrol.
If the Intent to Declare form is properly filled out, students will be automatically registered for the Seminar Course ROB1830Y on Acorn (on Sept 10, Jan 10, and May 1)

Submit the Intent to Pursue Form by

September 5

January 5

April 25

Submit the Completion Form By

December 10

For November Convocation

May 10

For March Convocation (in absentia)

August 1

For June Convocation

Recognition

Students who complete the program will receive the notation “Completed Collaborative Specialization in Robotics” on their transcript.