Robotics is a cross-disciplinary topic that spans multiple academic departments, programs, and concentrations. Depending on your focus, you can take courses in mechanical engineering, computer science, or courses that look at the philosophy of robotics, human cognition, or animal behavior as it applies to robotics. Courses are also offered that focus on a specific application of robotics, such as surgical or manufacturing. Here is a sampling of some of these courses.

    • MECE E4602y Introduction to robotics. We think of Robotics as the science of building devices that physically interact with their environment. The most useful robots do it precisely, powerfully, repeatedly, tirelessly, fast, or some combinations of these. The most interesting robots maybe even do it intelligently. This class covers the fundamentals of robotics, focusing on both the mind and the body. It includes the following topics: representation of spatial relationships; robot arms, manipulation and grasping; basic control for robot joints; mobile robots and navigation; sensing: vision, range sensing, tactile sensing and proprioception; complete robot systems; present and future applications for robots.


    • MECE 4611 E Robotics Studio. Prerequisites: Knowledge of basic computer programming (e.g. Java, Matlab, Python), or instructor's permission. A hands-on studio class exposing students to practical aspects of the design, fabrication, and programming of physical robotic systems. Students will experience the entire robot creation process, covering conceptual design, detailed design, simulation and modeling, digital manufacturing, electronics and sensor design, and software programming.


    • MECE E6614y Advanced topics in robotics and mechanism synthesis. Prerequisites: Prerequisite: APMA E2101APMA E3101MECE E4602 (or COMS W4733) Recommended: MECE E3401 or Instructor's Permission Kinematic modeling methods for serial, parallel, redundant, wire-actuated robots and multifingered hands with discussion of open research problems. Introduction to screw theory and line geometry tools for kinematics. Applications of homotropy continuation methods and symbolic-numerical methods for direct kinematics of parallel robots and synthesis of mechanisms. Course uses textbook materials as well as a collection of recent research papers. 


    • MECS E6615x or y Robotic Manipulation: Sensing, Planning, Design and Execution. Prerequisites: MECE E4602 or COMS W4733 Theory and mechanisms of robotic manipulation, from sensor data,reasoning and planning to implementation and execution. Grasp quality measures andoptimization; planning and execution for manipulation primitives; sensor modalities: vision, touch and proprioception; simulation for manipulation planning; design of robot manipulators. Grading based on a combination of class presentations of novel research results in the field, participation in discussions, and course projects combining simulation, processing of sensor data, planning for manipulation, design and implementation on real robot hands. 


    • MECE 6616 E Robot Learning. Robots using machine learning to achieve high performance in unscripted situations. Dimensionality reduction, classification and regression problems in robotics. Deep Learning: Convolutional Neural Networks for robot vision, Recurrent Neural Networks, and sensorimotor robot control using neural networks. Model Predictive Control using learned dynamics models for legged robots and manipulators. Reinforcement Learning in robotics: model-based and model-free methods, deep reinforcement learning, sensorimotor control using reinforcement learning.


    • COMS 4731 Computer Vision I. Prerequisites: the fundamentals of calculus, linear algebra, and C programming. Students without any of these prerequisites are advised to contact the instructor prior to taking the course. Introductory course in computer vision. Topics include image formation and optics, image sensing, binary images, image processing and filtering, edge extraction and boundary detection, region growing and segmentation, pattern classification methods, brightness and reflectance, shape from shading and photometric stereo, texture, binocular stereo, optical flow and motion, 2-D and 3-D object representation, object recognition, vision systems and applications.


    • COMS 4732 W Computer Vision II. Advanced course in computer vision. Topics include convolutional networks and back-propagation, object and action recognition, self-supervised and few-shot learning, image synthesis and generative models, object tracking, vision and language, vision and audio, 3D representations, interpretability, and bias, ethics, and media deception.


    • COMS W 4733 Computational Aspects of Robotics. Prerequisites: COMS W3134W3136, or W3137. Introduction to robotics from a computer science perspective. Topics include coordinate frames and kinematics, computer architectures for robotics, integration and use of sensors, world modeling systems, design and use of robotic programming languages, and applications of artificial intelligence for planning, assembly, and manipulation. 


    • COMS 6998 Topics in Robot Learning. This is an advanced seminar course that will focus on the latest research in machine learning for robotics. More specifically, we study how machine learning and data-driven method can influence the robot’s perception, planning, and control. For example, we will explore the problem of how a robot can learn to perceive and understand its 3D environment, how they can learn from experience to make reasonable plans, and how they can reliably act upon with the complex environment base on their understanding of the world. Students will read, present, and discuss the latest research papers on robot learning as well as obtain experience in developing a learning-based robotic system in the course projects.