CSC 375H5: Algorithmic Intelligence in Robotics

Robots of the future will need to operate autonomously in unstructured and unseen environments. It is imperative that these systems are built on intelligent and adaptive algorithms. This course will introduce fundamental algorithmic approaches for building an intelligent robot system that can autonomously operate in unstructured environments such as homes and warehouses. This course introduces the broad philosophy of “Sense-Plan-Act”, and covers algorithms in each of these areas -- how should the robot perceive the world, how to make long term decisions and how to perform closed-loop control of articulated robots.

Previous offering: Fall 2020

CSC 376H5: Fundamentals of Robotics

An introduction to robotics covering basic methodologies, tools, and concepts to build a foundation for advanced topics in robotics. This undergraduate-level course covers robot manipulators; kinematics; motion planning; and control. Topics covered in lecture will be implemented and explored in a practical environment using robots from different application domains.

Next offering: Fall 2023

CSC 475H5: Introduction to Reinforcement Learning

This course provides an introduction to reinforcement learning intelligence, which focuses on the study and design of agents that interact with a complex, uncertain world to achieve a goal. The course covers Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). Applications to computer vision, robotics, etc. are explored, and common RL algorithms are analyzed and implemented.

Previous offering: Fall 2021

CSC 476H5: Introduction to Continuum Robots

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.

Cross-Listed as a graduate course CSC 2606

Next offering: tba (can be taken as reading course)

CSC 477H5: Introduction to Mobile Robotics

This undergraduate-level course provides an introduction to robotic systems from a computational perspective. A robot is regarded as an intelligent computer that can use sensors and act on the world. We will consider the definitional problems in robotics and look at how they are being solved in practice and by the research community. The emphasis is on algorithms, probabilistic reasoning, optimization, inference mechanisms, and behavior strategies, as opposed to electromechanical systems design.

Previous offering: Fall 2021

CSC 478H5: Robotic Perception

This undergraduate-level course focuses on perception algorithms for robotics applications and sensors. The aim is to provide an understanding of the challenges encountered when deploying perception algorithms on a robot and introduce some of the tools and algorithms typically used to address these challenges. The algorithms will also be implemented and evaluated using real-world data from common use-cases.

Next offering: Winter 2023

CSC 496H5: Topics in Robotics

Introduction to a topic of current interest in robotics intended for CSC majors and specialists. Content will vary from year to year but will always maintain a robotics focus. The contact hours for this course may vary in terms of contact type (L, T, P) from year to year, but will be between 24-48 contact hours in total. See the UTM Timetable.

Next offering: Winter 2023 (Topic: Introduction to Medical Robotics)

CSC 2547: Topics in Machine Learning - Methods in 3D and Geometric Deep Learning

This course introduces deep learning methods and modern advances in 3D Vision. We will study representations, learning algorithms and generative models for 3D vision tasks at object and scene level. We will then study Geometric Deep Learning and concepts of Manifold Learning as relevant to Deep Learning. The 3D nature of this topic has many potential applications in graphics, robotics, content creation, mixed reality, biometrics, and more. At the end of this course, you will be familiar with: 1. Representation, Learning and Generative Deep Learning of Point Cloud Input 2. Differentiable and Neural Rendering 3. Geometric Deep Learning 4. Applications of Non-Euclidean Deep Learning outside Computer Vision.

Previous offering: Winter 2021

CSC 2606: Introduction to Continuum Robots

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.

Cross-Listed as an undergraduate course CSC476H5

CSC 2621: Topics in Robotics - Reinforcement Learning in Robotics

Robots of the future will need to operate autonomously in unstructured and unseen environments. It is imperative that these systems are built on intelligent and adaptive algorithms. Learning by interaction through reinforcement offers a natural mechanism to postulate these problems. This graduate-level seminar course will cover topics and new research frontiers in reinforcement learning (RL). Planned topics include: Model-Based and Model-Free RL, Policy Search, Monte Carlo Tree Search, off-policy evaluation, temporal abstraction/hierarchical approaches, inverse reinforcement learning and imitation learning.

Previous offering: Winter 2020

CSC 2626H: Imitation Learning for Robotics

This graduate-level 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. We 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.

Previous offering: Winter 2021

CSC 2621H: Topics in Robotics: Surgical Robotics and Image-Guided Therapy

This graduate level seminar “Topics in Robotics: Surgical Robotics and Image-Guided Therapy” will analyse the state-of-the-art based on recent papers and books in the domain of interventional assistance technology for physicians. Research of autonomous subtask execution by medical robots as well as decision support for image-guided procedures are highly influenced by vision-based perception of the surgical scene including motion compensation for soft tissue. Students will present state-of-the-art work, implement a related project, and give a final presentation.

Previous offering: Winter 2021