FORW-RD Courses

WR 593: Robot/AI Ethics and the Future of Work – THIS IS A FORW-RD NRT REQUIRED COURSE
Faculty: Dr. Yunus Telliel
Type: Semester-long (3 credits) – Fall & Spring
Course Description: Robotics and AI systems are transforming our world in remarkable and unpredictable ways. From robots in medicine and nursing, to self-driving cars, to automated decision making in human resource management, new technologies shape how we create, exchange, and distribute value. As such, these technologies raise questions about the future of work and workers: will AI, robotics, and other ‘autonomous and intelligent systems’ make human labor obsolete? Or, are we more likely to see a continuous demand in human skills and capabilities despite the push for automation? Will future jobs be able to foster human creativity and ingenuity? Can new technologies be a remedy to existing inequities and injustices, or only make them more acute? The course addresses these and similar questions from the perspective of design and technology ethics. The course will include an introduction to design and technology ethics, an introduction to general trends in changing ecosystems of work and technology, a closer look at the human-machine interface in different types of industries, and case studies focusing on the integration of anticipatory ethics into the design of AI or robotic technologies. The final deliverable for this course will be a draft of a thesis/dissertation chapter/section that focses on broader impacts, social implications, research ethics, or related issues in students’ graduate projects. As part of this course, students will also learn to apply National Science Foundation’s (NSF) broader impacts framework to their graduate projects and future grant applications.

ME/RBE 530: Soft Robotics
Faculty: Dr. Cagdas Onal 
Type: Term-long (2 credits) – D term 
Course Description: Soft robotics studies ‘intelligent’ machines and devices that incorporate some form of compliance in their mechanics.  Elasticity is not a byproduct but an integral part of these systems, responsible for inherent safety, adaptation and part of the computation in this class of robots.  The course will cover a number of major topics of soft robotics including but not limited to design and fabrication of soft systems, elastic actuation, embedded intelligence, soft robotic modeling and control, and fluidic power.  Students will learn design and fabrication methodologies of soft robots, review and discuss articles in the field, and complete a literature survey to supplement the course material. 

ME 593/MTE 594: Printed Electronics & Sensors
Faculty: Dr. Pratap Rao 
Type: Term-long (2 credits) – D Term
Course Description: This course will serve as an introduction to printed electronics and sensors, including flexible and stretchable devices, those that are fully printed, and hybrid devices that consist of printed circuits and attached components such as microchips. Processes for printing circuits and functional components, including micro-dispense, aerosol jet, inkjet, screen, and gravure, will be explored with consideration of their respective advantages and disadvantages in terms of printing resolution, reliability, and speed. Printed materials including metals, polymers, and semiconductors in the form of nano-materials or reactive inks and pastes will be considered, as will flexible and stretchable plastic and elastomeric substrates. A number of printed sensor applications will be reviewed, including wearable physiological and medical sensors, automotive and aerospace sensors, and approaches to reduce size, weight, power, and cost will be considered. Energy storage and energy harvesting approaches will also be explored. The course will consist of lectures, reading journal articles, and a course project.

MIS 583: User Experience Applications 
Faculty: Dr. Soussan Djamasbi 
Type: Semester-long (3 credits) – Spring 
Course Description: The UX Applications course provides an introduction to using UX methods to study user experience. The course teaches students how to use the newest research tools, including eye tracking and emotion detection, to study user experiences of technological products and services. Students will learn how businesses can benefit from these techniques. Both theoretical concepts and practical skills will be addressed within the scope of the class through hands-on projects, class exercises, and assignments. 

MIS 585: User Experience Design  
Faculty: Dr. Soussan Djamasbi 
Type: Semester-long (3 credits) – Fall 
Course Description: Designing positive user experiences is becoming increasingly important in staying competitive in the marketplace. This UX Design course offers students hands-on experiences, through the use of real-world projects, that provide them with a strong portfolio of work that showcases their skills in UX/UI, visual, service, experience, and product design. Throughout this course, students will create innovative experiences that enrich their technical fluency in both web and interactive development. The course provides a foundation in art and design in order to help students articulate their work to stakeholders and translate outcomes as business value. 

MIS 586: User Experience (UX) Research Methods
Faculty: Dr. Soussan Djamasbi 
Type: Semester-long (3 credits) – Fall 
Course Description: In today’s digital economy, understanding how people use and experience technology is crucial to designing successful technological products and services. The UX research methods course covers tools and techniques for identifying, defining, designing, and testing innovative user-centered technologies. Students learn to design and conduct various types of user studies, from unstructured and structured interview studies to usability and user experience experiments. The course also covers advance topics in UX research such as eye-tracking for the objective measurement of user attention, engagement, and information processing behavior.

RBE 460X – Human Factors and Human-Robot Interface  
Faculty: Dr. Jane Li 
Type: Term-long, (2 credits) – C term 
Course Description: This is an introductory course on human-robot interaction, offered to first year graduate students and senior undergraduate students. It will introduce (1) the behavior and preference of human motor control and motor learning, and (2) how they influence the design of human-robot interface and the dynamics of human-robot interaction. Students will also learn how to conduct human movement studies and social science studies for the design and evaluation of human-robot interfaces. Students in this course will work on interdisciplinary projects, with the experts in robotics, social science, nursing, and education.

RBE 450X: Vision-based Robotic Manipulation 
Faculty: Dr. Berk Calli
Type: Term-long (2 credits) – A term
Course Description: This course focuses on the role of visual sensing in robotic manipulation. It covers fundamental manipulation concepts such as grasp matrix, grasp taxonomies and grasp stability metrics. Various grasp planning strategies in the literature is studied. 2D and 3D vision-based control algorithms are covered. Point cloud processing techniques that allow object detection, segmentation and feature extraction are studied and utilized. Integrating all these aspects, the whole vision-based robotic manipulation pipeline is designed. 

RBE/CS 526 – Human-Robot Interaction 
Faculty: Dr. Jane Li 
Type: Semester-long (3 credits) – Fall 
Course Description: This course will introduce the fundamental concepts and theories of human-robot interaction, as well as research methodologies. The lecture topics include: (1) framework of human-robot teaming; (2) level of autonomy; (3) human-robot interfaces for direct and supervisory control; (4) methods and metrics for evaluating interface usability; and (5) user study design. It will refer to the recent research papers to exemplify the design of human-robot interfaces and robot autonomy for cognitive and physical assistance, as well as the interfaces and approaches for robot learning from human demonstrations. Students in this course will work on (1) Individual Paper Reading and Team Literature review; (2) Algorithm implementation; and (3) Courses Projects of design and/or evaluation human-robot interfaces

RBE 595: Haptic and Robotic Interaction 
Faculty: Dr. Jing Xiao
Type: Term-long (2 credits) – A term
Course Description: The course is focused on studying how to detect and simulate physical interaction between two entities (for example, between a robot and an object, or between two objects) in a virtual environment, motivated by applications in haptics, where a human operator interacts with virtual objects via a haptic display device. Applications range from virtual training for a wide range of tasks that require physical interaction with objects, such as dental and surgical operations, to teleoperation of robotic manipulation tasks through haptics, and dynamic simulation. Multi-region collisions and contacts involve both rigid and deformable objects will be addressed. Prerequisite: Not determined yet, but background in data structure and programming is needed.  

RBE 595/CS 525: Swarm Intelligence
Faculty: Dr. Carlo Pinciroli
Type: Semester-long (3 credits) – Spring
Course Description: This course will cover a wide range of topics in swarm intelligence, including mathematical, computational, and biological aspects. The course is organized in four parts. In the first part, the students will learn about complex systems and the basic concepts of self-organization, such as positive and negative feedback, symmetry breaking, and emergence. The second part concerns several types of network models, such as information cascades, epidemics, and voting. The instructor will illustrate a diverse collection of self-organized systems in nature, finance, and technology that concretize these concepts. The third part is dedicated to swarm robotics, and will cover common swarm algorithms for task allocation, collective motion, and collective decision-making. The fourth and final part covers optimization algorithms inspired by swarm intelligence, namely ant colony optimization and particle swarm optimization. The course will blend theory and practice, challenging the students to learn by implementing the algorithms discussed in class. The final project will involve working on a research problem in swarm robotics, and the final deliverable will include a demo and a research paper.