Get your Graduate Emphasis in Robotics
For MEng Students
New Emphasis Updates
Starting Fall 2022, we've launched the Collaborative Specialization in Robotics for research stream (MASc/MSc/PhD) students. Research students who started their degree after Fall 2022 can only pursue the Collaborative Specialization in Robotics.
Research students who started their degree prior to Fall 2022 can still pursue the Emphasis in Robotics. You may be able to complete the Collaborative Specialization instead if you meet the requirements.
For questions about the Emphasis in Robotics or the Collaborative Specialization in Robotics, contact robotics.gradstudies@utoronto.ca for more information. For questions about course enrolment, please contact your department graduate program advisor.
Eligibility
Any student already pursuing a graduate degree (MEng, MASc, PhD) from one of our qualifying Departments or Institutes in the Faculty of Applied Science and Engineering (FASE) is eligible for the Graduate Emphasis in Robotics.
Qualifying Departments & Institutes
Not yet a student at UofT?
You must already be enrolled in a MASc or MEng or PhD program at one of our qualifying Departments or Institutes at UofT in order to be eligible for the Graduate Emphasis in Robotics.
If you are not yet a graduate student at UofT, check out the admissions pages of our partner departments!
Are you a Computer Science Student at UofT pursuing robotics?
The University of Toronto is actively building its computer science presence in robotics, and has recently launched a CS-focused robotics research cluster at the UTM campus.
CS students are welcome to enrol in the Collaborative Specialization in Robotics.
Course Requirements
To receive the Robotics Emphasis, eligible students must successfully complete four courses (2.0 full-course equivalents [FCEs]) chosen from at least two course groups, and no more than two in any given group. Note that not all courses will be offered each year.
Pre-approved substitutions are listed below. Each student will only be allowed one substitution.
Group 1: Planning and Control
Fundamentals of linear time-invariant control systems. State space modeling and control design, controllability, stabilization, pole placement controllers, observability, Kalman filters, observer design, optimal control, tracking controllers. Labs: real-time control experiments on design techniques.
Prerequisite: ECE356H1.
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.
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.
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.
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.
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.
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.
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.
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.
Pre-approved Group 1 substitutions
A course covering selected topics. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
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
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.
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.
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.
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.
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.
A course covering selected topics in Machine Learning not covered in other courses. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
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.
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.
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.
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.
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.
An introduction to aspects of computer vision specifically relevant to robotics applications. Topics include the geometry of image formation, basic image processing operations, camera models and calibration methods, image feature detection and matching, stereo vision, structure from motion and 3D reconstruction. Discussion of moving object identification and tracking as time permits.
Pre-approved Group 2 substitutions
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.
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.
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.
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.
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
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.
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.
Multi-degree of freedom systems, using both analytical and approximate methods. Vibrations of continuous systems, including strings, bars and membranes. Natural modes of plate vibration – approximate methods such as Rayleigh’s Energy Methods, Rayleigh-Ritz Method, Galerkin’s Method, and assumed mode method. Introduction to finite element analysis.
General perspective of neural engineering and neurobiology; biological neural networks; parametric neural models using rate processes; nonparametric neural models, using the Volterra-Wiener approach; artificial neural networks as nonparametric neural models.
Pre-approved Group 3 substitutions
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
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.
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.
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
A course covering selected topics in Robotics not covered in other courses. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
Classification of robot manipulators, kinematic modeling, forward and inverse kinematics, velocity kinematics, path planning, point-to-point trajectory planning, dynamic modeling, Euler-Langrange equations, inverse dynamics, joint control, computed torque control, passivity-based control, feedback linearization.
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.
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.
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
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
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
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.
Pre-approved Group 4 substitutions
This course builds on the concepts of AI Applications in Robotics I.
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.
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.
Group 1: Planning and Control
Fundamentals of linear time-invariant control systems. State space modeling and control design, controllability, stabilization, pole placement controllers, observability, Kalman filters, observer design, optimal control, tracking controllers. Labs: real-time control experiments on design techniques.
Prerequisite: ECE356H1.
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.
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.
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.
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.
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.
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.
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.
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.
Pre-approved Group 1 substitutions
A course covering selected topics. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
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
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.
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.
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.
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.
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.
A course covering selected topics in Machine Learning not covered in other courses. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
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.
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.
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.
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.
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.
An introduction to aspects of computer vision specifically relevant to robotics applications. Topics include the geometry of image formation, basic image processing operations, camera models and calibration methods, image feature detection and matching, stereo vision, structure from motion and 3D reconstruction. Discussion of moving object identification and tracking as time permits.
Pre-approved Group 2 substitutions
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.
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.
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.
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.
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
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.
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.
Multi-degree of freedom systems, using both analytical and approximate methods. Vibrations of continuous systems, including strings, bars and membranes. Natural modes of plate vibration – approximate methods such as Rayleigh’s Energy Methods, Rayleigh-Ritz Method, Galerkin’s Method, and assumed mode method. Introduction to finite element analysis.
General perspective of neural engineering and neurobiology; biological neural networks; parametric neural models using rate processes; nonparametric neural models, using the Volterra-Wiener approach; artificial neural networks as nonparametric neural models.
Pre-approved Group 3 substitutions
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
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.
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.
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
A course covering selected topics in Robotics not covered in other courses. Different topics may be covered each year depending on the interest of the Staff and students. May not be offered every year.
Classification of robot manipulators, kinematic modeling, forward and inverse kinematics, velocity kinematics, path planning, point-to-point trajectory planning, dynamic modeling, Euler-Langrange equations, inverse dynamics, joint control, computed torque control, passivity-based control, feedback linearization.
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.
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.
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
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
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
Pre-approved Group 4 substitutions
This course builds on the concepts of AI Applications in Robotics I.
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.
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.
Application Deadlines
Be sure to complete and submit the FASE Emphasis Completion Request Form prior to your convocation date.
Learn more about the steps to apply for the emphasis below.
October 1
For November Convocation
February 1
For March Convocation
(in absentia)
May 1
For June Convocation
Get the Emphasis
1
Successfully complete the required coursework
Ensure you have successfully completed the course requirements for the emphasis: four courses from at least two course groups.
2
Submit your request before you graduate
Complete and submit the FASE Emphasis Completion Request Form prior to your convocation date. See deadlines above.
3
Graduate Studies office verifies your transcript
Wait while the Office of the Vice Dean Graduate Studies (Engineering) verifies your transcripts to ensure that you have completed the required courses and have received final marks.
4
Robotics Institute approves your request
Provided Office of the Vice Dean Graduate Studies (Engineering) has deemed that you have satisfied the emphasis requirements, the Robotics Institute will approve your request.
5
"Robotics Emphasis" is noted on your transcript!
The Office of the Vice Dean Graduate Studies (Engineering) will notate the Robotics emphasis on your transcript: “Completed - YEAR SEMESTER- Emphasis (Degree): Robotics”