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Robotics Institute Courses

See also: Carnegie Mellon Current Courses

This Page: MSIT-RT Courses

All courses with a "16-" prefix are offered by the Robotics Program. Other departments offering courses taught by Robotics faculty are Computer Science (CS), Electrical and Computer Engineering (ECE), Mechanical Engineering (MechE), Statistics (Stat), Psychology (Psych), the Tepper School of Business (GSIA), and the Institute for Complex Engineered Systems (ICES).

10-701: Machine Learning (CS 15-781)
Instructor: Carlos Guestrin
Units: 12
Semester: Fall and Spring
This course is targeted at graduate students who need to learn about current-day research, and about how to perform current-day research, in Artificial Intelligence---the discipline of designing intelligent decision-making machines.

Techniques from Probability, Statistics, Economics, Algorithms, Operations Research and Optimal Control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots surveying Antarctica, schedulers moving billions of dollars of inventory, spacecraft deciding which experiments to perform, or vehicles negotiating for lanes on the freeway. This AI course is a review of a selected set of these tools. The course will cover the ideas underlying these tools, their implementation, and how to use them or extend them in your research.

15-381: Artificial Intelligence: Representation and Problem Solving (CS)
Instructors: Martial Hebert, Mike Lewicki
Units: 12
Semester: Spring
This course is about the theory and practice of Artificial Intelligence. We will study modern techniques for computers to represent task-relevant information and make intelligent (i.e. satisficing or optimal) decisions towards the achievement of goals. The search and problem solving methods are applicable throughout a large range of industrial, civil, medical, financial, robotic, and information systems. We will investigate questions about AI systems such as: how to represent knowledge, how to effectively generate appropriate sequences of actions and how to search among alternatives to find optimal or near-optimal solutions. We will also explore how to deal with uncertainty in the world, how to learn from experience, and how to learn decision rules from data. We expect that by the end of the course students will have a thorough understanding of the algorithmic foundations of AI, how probability and AI are closely interrelated, and how automated agents learn. We also expect students to acquire a strong appreciation of the big-picture aspects of developing fully autonomous intelligent agents. Other lectures will introduce additional aspects of AI, including natural language processing, web-based search engines, industrial applications, autonomous robotics, and economic/game-theoretic decision making.

Prerequisites: 15211

15-384: Robotic Manipulation (CS)
Instructor: George Kantor
Units: 12
Semester: Fall
Foundations and principles of robotic manipulation. Topics include computational models of objects and motion, the mechanics of robotic manipulators, the structure of manipulator control systems, planning and programming of robot actions.

Prerequisites: (15-111 or 15-200) and (18-202 or 21-241 or 24-311)

15-385: Computer Vision (CS)
Instructors: Srinivasa Narasimhan, Tai-sing Lee
Units: 12
Semester: Spring
Basic concepts in machine vision, including sensing and perception, 2D image analysis, pattern classification, physics-based vision, stereo and motion, and solid model recognition.

Prerequisites: 15-213, 21-214, and 21-259

15-462: Computer Graphics I (CS)
Instructor: Nancy Pollard, Alexei Efros
Units: 12
Semester: Fall
This course provides a comprehensive introduction to computer graphics modeling, animation, and rendering. Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing.

Prerequisites: (15-213 and 21-241 and 21-259) or (15-213 and 18-202)

15-491: CMRobotBits: Creating Intelligent Robots (CS)
Instructors: Manuela Veloso, Brett Browning
Units: 9
Semester: Fall
Creating intelligent robots can be viewed as the integration of many "bits," "RoboBits," (therefore the name CMRoboBits -- CM for Carnegie Mellon). This course will teach students these "RoboBits" for creating both single robots and groups of intelligent robots. RoboBits achieve the necessary robot capabilities for their perception, cognition, and action. We will use concrete robots, such as the Sony AIBO robots, the ER1s, and other existing robots at CMU, to understand in depth the issues involved in developing such capabilities in a robot and in a group of robots. We will focus on vision processing, object recognition, robot legged and wheeled motion, cognitive architectures, planning, learning, and teamwork among robots and between robot and humans. The course will have a 2h weekly lecture and a 1h weekly recitation/lab session. The course will be primarily hands-on work with weekly homeworks which incrementally build up to the complete robot and robot teams. The homeworks also include a part with questions on the lectured material. There will be a final project, proposed by the students or selected from a list proposed by the instructors, in which the robots perform a complete task. Evaluation will be based on the homeworks, an in-class midterm exam, and the final project.

Prerequisites: 15211

15-780: Graduate Artificial Intelligence
Instructors: Ziv Bar-Joseph, Geoff Gordon
Units: 12
Semester: Fall
This course is targeted at graduate students who need to learn about current-day research, and about how to perform current-day research, in Artificial Intelligence---the discipline of designing intelligent decision-making machines.

Techniques from Probability, Statistics, Economics, Algorithms, Operations Research and Optimal Control are increasingly important tools for improving the intelligence and autonomy of machines, whether those machines are robots surveying Antarctica, schedulers moving billions of dollars of inventory, spacecraft deciding which experiments to perform, or vehicles negotiating for lanes on the freeway. This AI course is a review of a selected set of these tools. The course will cover the ideas underlying these tools, their implementation, and how to use them or extend them in your research. Please refer to this link for the most recent schedule updates.

15-862: Computational Photography (CS)
Instructor: Alexei Efros
Units: 12
Semester: Fall
Computational Photography is an emerging new field created by the convergence of computer graphics, computer vision and photography. Its role is to overcome the limitations of the traditional camera by using computational techniques to produce a richer, more vivid, perhaps more perceptually meaningful representation of our visual world. The aim of this advanced undergraduate course is to study ways in which samples from the real world (images and video) can be used to generate compelling computer graphics imagery. We will learn how to acquire, represent, and render scenes from digitized photographs. Several popular image-based algorithms will be presented, with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming assignments, in which students will have the opportunity to acquire their own images of indoor and outdoor scenes and develop the image analysis and synthesis tools needed to render and view the scenes on the computer.

Prerequisites: 15-213, 21-214, and 21-259

15-883: Computational Models of Neural Systems (CS)
Instructor: Touretzky
Units: 12
Semester: Fall
This course is an in-depth study of information processing in real neural systems from a computer science perspective. We will examine several brain areas where processing is sufficiently well understood that it can be discussed in terms of specific representations and algorithms. We will focus primarily on computer models of these systems, after establishing the necessary anatomical, physiological, and psychophysical context. There will be some neuroscience tutorial lectures for those with no prior background in this area.

16-221: Robots to the Rescue: A Gentle Introduction to Mobile Robotics
Instructor: Raj Reddy
Units: 12 or 18
Semester: Spring, Summer, Fall
This course has been designed to teach the basic tools and techniques of engineering and programming a mobile robot. Student teams will build an autonomous mobile robot (from kits that will be provided), and learn to program it to perform increasingly sophisticated behaviors. Besides providing an introduction to autonomous mobile robot technologies, the students also learn key concepts of mechanics, electronics, programming, and systems design and integration. Maybe most important, the students will learn how to use the system for solving interesting and challenging problems in rescue robotics.

Programming experience desirable.

16-264: Humanoids
Instructors: Christopher Atkeson
Units: 12
Semester: Spring '08 (Not offered on a regular basis)
This course will survey work on humanoid robots and simulated humans in movies, games and other applications. Topics will be taken from perception including visual, auditory, and tactile perception, cognition including reacting, planning, and learning, and movement generation including kinematics, dynamics, control, manipulation, and bipedal locomotion.

16-299: Introduction to Feedback Control Systems
Instructor: George Kantor
Units: 12
Semester: Spring
This course is designed as a first course in feedback control systems for computer science majors. Course topics include classical linear control theory (differential equations, Laplace transforms, feedback control), linear state-space methods (controllability/observability, pole placement, LQR), nonlinear systems theory, and an introduction to control using computer learning techniques. Laboratory work includes implementation of controllers robotic devices. Priorities will be given to computer science majors with robotics minor.

16-311: Introduction to Robotics
Instructor: Howie Choset
Units: 12
Semester: Spring
This course presents an overview of robotics in practice and research with topics including vision, motion planning, mobile mechanisms, kinematics, inverse kinematics, and sensors. In course projects, students construct robots which are driven by a microcontroller, with each project reinforcing the basic principles developed in lectures. Students nominally work in teams of three: an electrical engineer, a mechanical engineer, and a computer scientist. This course will also expose students to some of the contemporary happenings in robotics, which includes current robot lab research, applications, robot contests and robots in the news.

16-421: Vision Sensors
Instructor: Srinivasa Narasimhan
Units: 12
Semester: Spring
This course covers the fundamentals of vision cameras and other sensors - how they function, how they are built, and how to use them effectively. The course presents a journey through the fascinating five-hundered-year history of "camera-making" from the early 1500's "camera obscura" through the advent of film and lenses, to today's mirror-based and solid-state devices. The course includes a significant hands-on component where students learn how to use the sensors and understand, model and deal with the uncertainty (noise) in their measurements. While the first half of the course deals with conventional "single viewpoint" or "perspective" cameras, the second half of the course covers much more recent "multi-viewpoint" or "multi-perspective" cameras that include an array of lenses and mirrors. These sensors provide unusual and compelling forms of visualizations of the world around us that also drive new display technologies.

The course is open to all students in SCS and ECE.

Prerequisites: Linear Algebra and Calculus.

16-597: Undergraduate Reading and Research
Need project supervisor's permission.

16-711: Kinematics, Dynamic Systems and Control
Instructor: Chris Atkeson
Units: 12
Semester: Spring
Basic concepts and tools for the analysis, design, and control of robotic mechanisms. Topics covered include foundations of kinematics, kinematics of robotic mechanisms, review of basic systems theory, control of dynamical systems. Advanced topics will vary from year-to-year, including motion planning and collision avoidance, adaptive control, and hybrid control.

16-720: Computer Vision
Instructor: Martial Hebert
Units: 12
Semester: Fall
This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of the world with the goal of reconstructing and understanding the objects and processes in the world that are producing them. The emphasis is on physical, mathematical, and information processing aspects of vision.

Topics covered include image formation and representation, camera geometry and calibration, multi-scale analysis, segmentation, contour and region analysis, energy-based techniques, reconstruction of based on stereo, shading and motion, 3-D surface representation and projection, and analysis and recognition of objects and scenes using statistical and model-based techniques. The material is based on a recent graduate-level textbook augmented with research papers, as appropriate. The course involves considerable Matlab programming exercises.

Textbook Information:
Title: Computer Vision: A Modern Approach
Authors: David Forsyth and Jean Ponce
Publisher: Prentice Hall
ISBN: 0-13-085198-1

16-721: Learning-based Methods in Vision
Instructor: Alexei Efros
Units: 12
Semester: Spring
A graduate seminar course in Computer Vision with emphasis on using large amounts of real data (images, video, textual annotations, user preferences, etc) to learn the structure of our visual world toward the ultimate goal of Image Understanding. We will be reading an eclectic mix of classic and recent papers on topics including: theories of perception, low-level vision (color, texture), mid-level vision (grouping and segmentation), object and scene recognition, image parsing, words and pictures models, image manifolds, etc.

Prerequisites: Graduate Computer Vision

16-722: Sensing and Sensors
Instructor: Mel Siegel
Units: 12
Semester: Spring
We will study quantitative perception - i.e., sensing - illustrated by the devices - i.e., sensors - that implement them. By analysing robotics applications we will learn to specify necessary sensor characteristics, to analyze whether they can be realized, to compare what can be realized in principle with what can actually be built, and to design transducing, digitizing, and computing systems that come close to realizing the ideal.

We will study the sensing requirements of robot functionality - manipulation and mobility - and robot applications - medical, security, biometrics, earth and planetary exploration, etc. In the Spring 2007 edition of the course we will continue the model that has proven popular and successful in the last three editions. The underlying principle is that the instructor teaches the students about sensing - the underlying science and engineering principles - and the students teach each other about sensors - the most current devices and systems that can be purchased off-the-shelf, and the most advanced devices and systems that have recently been prototyped in the most advanced university and government labs.

Instead of the traditional lecture/reading/homework/examination course format, after a few comprehensive lectures to provide the required background and set the tone, subsequent lectures will provide an overview of the topical area and supplementary reading materials, and the students will fill in the details by taking on instructor-guided preparation and presentation of approximately half-hour slide-illustrated lectures with pointers to up-to-date supplementary reading materials. Each student-lecturer's responsibility will also include generating a homework assignment taking off from his/her presentation, and grading it under the instructor's guidance. There will be no timed exams, but there may be an open-ended final exam if it is required to determine final grades fairly.

16-725: Methods in Medical Image Analysis
Instructors: John Galeotti
Units: 12
Semester: Spring
The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the National Library of Medicine Insight Toolkit (ITK), a new software library developed by a consortium of institutions including CMU. In addition to image analysis, the course will describe the major medical imaging modalities and include interaction with practicing radiologists at UPMC.

Prerequisites: There will be no required text. FOR S08, THIS CLASS WILL BE HELD AT THE UNIVERSITY OF PITTSBURGH, BENEDUM F63.. Prerequisites: Permission of the instructor (contact galeotti+miia@cs.cmu.edu), knowledge of C++, vector calculus and basic probability.

16-735: Robotic Motion Planning
Instructor: Howie Choset
Units: 12
Semester: (not offered on a regular basis)
The robot motion field and its applications have become incredibly broad and theoretically deep at the same time. The goal of the course is to provide an up-to-date foundation in the motion planning field, make the fundamentals of motion planning accessible to the novice and relate low-level implementation to high-level algorithmic concepts. We cover basic path planning algorithms using potential functions, roadmaps and cellular decompositions. We also look at the recent advances in sensor-based implementation and probabalistic techniques, including sample-based roadmaps, rapidly exploring random trees, Kalman filtering, and Bayesian estimation.

16-741: Mechanics of Manipulation
Instructor: Matt Mason
Units: 12
Semester: Spring
Kinematics, statics, and dynamics of robotic manipulator's interaction with a task, focusing on intelligent use of kinematic constraint, gravity, and frictional forces. Automatic planning based on mechanics. Application examples drawn from manufacturing and other domains.

16-761: Introduction to Mobile Robots
Instructor: Al Kelly
Units: 12
Semester: Spring
This course covers all aspects of mobile robot systems design and programming from both a theoretical and a practical perspective. The basic subsystems of control, localization, mapping, perception, and planning are presented. For each, the discussion will include relevant methods from applied mathematics. aspects of physics necessary in the construction of models of system and environmental behavior, and core algorithms which have proven to be valuable in a wide range of circumstances.

16-778: Mechatronic Design (also ECE 18-578)
Instructor: John Dolan
Units: 12
Semester: Spring
Mechatronics is the synergistic integration of mechanism, electronics, and computer control to achieve a functional system. Because of the emphasis upon integration, this course is a semester-long multidisciplinary capstone hardware project design experience in which small teams of electrical and computer engineering, mechanical engineering and robotic students deliver an end-of-course demonstration of a final integrated prototypical system. Throughout the semester, the students configure, design, implement, test and evaluate in the laboratory several mechatronic devices and subsystems culminating in the final integrated system.

Lectures will complement the laboratory experience with comparative surveys, operational principles, and integrated design issues associated with the spectrum of mechanism, electronics, and control components.

16-811: Mathematical Fundamentals for Robotics
Instructor: Michael Erdmann
Units: 12
Semester: Fall
Fourier transforms, the Nyquist sampling theorem, differential equations, numerical methods, calculus of variations, differential geometry, and related topics.

16-822: Geometry-based Methods in Vision
Instructor: Martial Hebert
Units: 12
Semester: Spring
The course focuses on the geometric aspects of computer vision: the geometry of image formation and its use for 3D reconstruction and calibration. The objective of the course is to introduce the formal tools and results that are necessary for developing multi-view reconstruction algorithms. The fundamental tools introduced study affine and projective geometry, which are essential to the development of image formation models. Additional algebraic tools, such as exterior algebras are also introduced at the beginning of the course. These tools are then used to develop formal models of geometric image formation for a single view (camera model), two views (fundamental matrix), and three views (trifocal tensor); 3D reconstruction from multiple images; and auto-calibration.

Prerequisites: Computer Vision (16-721 or equivalent)

Book: The Geometry of Multiple Images, Faugeras and Long, MIT Press.

Topics covered:

• Fundamentals of projective, affine, and Euclidean geometries
• Invariance and duality
• Exterior and Grassman algebras
• Single view geometry: The pinhole model
• Calibration techniques
• 2-view geometry: The Fundamental matrix
• 2-view reconstruction
• 3-view geometry: The trifocal tensor
• Parameter estimation and uncertainty
• n-view reconstruction
• Self-calibration