Mechatronics, Controls, and Robotics Lab | NYU Tandon School of Engineering

Courses

Speciality Courses

Two courses from the same specialty area must be taken from the following list to develop a specialization

Assistive Mechatronic and Robotic Technologies

Robots for Disability (ROB-GY 6413):

3 Credits

This course will introduce personal, societal, and technological challenges related to physical disability, cognitive disability, and senior living. After an introduction to these challenges, students will learn about current state of art mechatronics and robotics solutions to handle these problems. Finally, they will apply their mechatronics and robotics learning to produce novel robotics solutions to address a specific problem related to a disability.

Prerequisite: Graduate standing and ROB-GY 5103 or advisor approval

Projects

Gait and Manipulation (ROB-GY 6313):

3 Credits

Review of fundamental robot kinematics, dynamics, and control. Types of robotic manipulation. Design and control of robotic manipulators. Robotic hand and arm. Robotic manipulation modeling, simulation, and experiments. Gait types of legged systems. Biped and quadruped systems. Human walking and running, and passive dynamics. Design and control of biped walking robots. Robotic gait modeling, simulation, and experiments. Focus on hands-on experience in design, fabrication, and control of simple mechanisms.

Prerequisite: Graduate standing and ROB-GY 6003 or advisor approval

Interactive Medical Robotics (ROB-GY 6423):

3 Credits

In this course, we will investigate the application, functionality, and theoretical aspects of the state-of-the-art interactive robotic technologies in medicine. The focus of the course will be on advanced surgical, and neurorehabilitative robotic systems. Technological aspects, such as instrumentation, actuation, mechanisms, imaging, and signal acquisition, will be introduced. Also, theoretical aspects related to control, dynamics, kinematics, haptics, stability, passivity, human-robot interaction, teleoperation, machine learning and bio-signal processing will be discussed in the context of medical robotic systems. Students are expected to be fluent in MATLAB and have solid background in at least two of the following four topics: signal processing, dynamics, control, robotics.

View Course Flyer (PDF)

Graduate standing and ME-GY 6703 or ROB-GY 6003 or advisor approval

Mobile Robotics

Robots Localization and Navigation (ROB-GY 6213):

3 Credits

This course presents the concepts, techniques, algorithms, and state-of-the-art approaches for robot perception, localization, and mapping. The course will show the theoretical foundations and will also have a substantial experimental component based on Matlab/ROS. The course will start from basic concepts in probability and then introduce probabilistic approaches for data fusion such as Bayes Filters, Kalman Filter, Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter. Then, the course will introduce the SLAM problem showing how this has recently been solved using batch optimization and graph methods. Finally, mapping algorithms will also be briefly discussed.

Prerequisite: Graduate standing and ME-GY 6923 or ME-GY 6703 or advisor approval

Swarm Robotics (ROB-GY 6333):

3 Credits

The student who completes this course will gain an advanced understanding of the analysis and control of networked dynamical systems, with a specific accent on networked robotic systems. He/she will be able to study the properties of networked robotic systems through the analysis of the intertwining properties of the network structure and of the individual dynamics of the single robot. Moreover, he/she will be able to understand and design algorithms for distributed control of teams of mobile agents and robots.

Prerequisite: Graduate standing and ME-GY 6703 or ROB-GY 6003 or advisor approval

Reinforcement Learning and Optimal Control for Robotics (ROB-GY 6323):

3 Credits

What kind of movements should a robot perform in order to walk, jump or manipulate objects? Can it compute optimal behaviors online? Can it learn this directly from trial and error? This course will introduce modern methods for robotics movement generation based on numerical optimal control and reinforcement learning. It will cover fundamental topics in numerical optimal control (Bellman equations, differential dynamic programming, model predictive control) and reinforcement learning (actor-critic algorithms, model-based reinforcement learning, deep reinforcement learning) applied to robotics. It will also contain hands-on exercises for real robotic applications such as walking and jumping, object manipulation or acrobatic drones. Recommended background in at least one of the following: linear systems; robotics; machine learning; convex optimization; programming (python).

Prerequisite: Graduate standing and ME-GY 6703 or ROB-GY 6003 or advisor approval

Robot Perception (ROB-GY 6203):

3 Credits

Smart automation systems (e.g., driverless cars, domestic/warehouse mobile robots, intelligent transportation systems, robotic construction machines, etc.) need to understand both their own poses and the surroundings to fulfil their tasks safely, accurately, and efficiently. This requires an intelligent extraction of both geometric and semantic information from sensory input (mainly visual sensors such as cameras/LIDAR). This course aims to combine the established theories of geometric vision and the recent progress in pattern recognition in the context of robotic/intelligent systems. Students will study and practice the basic theories of computer vision and machine learning through relevant applications. For example, pose estimation of a robotic agent from onboard cameras, 3D reconstruction for map creation, object detection/segmentation for obstacle avoidance, tracking for target following, place recognition from images when GPS is unreliable, and so on.

Prerequisite: Graduate standing or advisor approval