Robotics Engineering

Undergraduate Courses


Cat. I

Multidisciplinary introduction to robotics, involving concepts from the fields of
electrical engineering, mechanical engineering and computer science. Topics
covered include sensor performance and integration, electric and pneumatic
actuators, power transmission, materials and static force analysis, controls and
programmable embedded computer systems, system integration and robotic
applications. Laboratory sessions consist of hands-on exercises and team projects
where students design and build mobile robots.
Undergraduate credit may not be earned for both this course and for ES 2201.
Recommended background: mechanics (PH 1110/ PH 1111).


This course introduces students to the fundamental principles of programming as it applies to robotic applications. Topics include data structures, control flow, modularization, state machines, and event-based input/output. Students will be expected to implement, test, and debug programs and apply them to microcontrollers. Special focus will be given to writing efficient and reusable code. This course provides appropriate programming background for RBE 1001.


Cat. I

First of a four-course sequence introducing foundational theory and practice of
robotics engineering from the fields of computer science, electrical engineering
and mechanical engineering. The focus of this course is the effective conversion
of electrical power to mechanical power, and power transmission for purposes of
locomotion, and of payload manipulation and delivery. Concepts of energy,
power and kinematics will be applied. Concepts from statics such as force,
moments and friction will be applied to determine power system requirements
and structural requirements. Simple dynamics relating to inertia and the
equations of motion of rigid bodies will be considered. Power control and
modulation methods will be introduced through software control of existing
embedded processors and power electronics. The necessary programming concepts and interaction with simulators and Integrated Development
Environments will be introduced. Laboratory sessions consist of hands-on
exercises and team projects where students design and build robots and related

Recommended background: ES 2501 (can be taken
concurrently), and PH 1120 or PH 1121.


Cat. I

Second of a four-course sequence introducing foundational theory and practice
of robotics engineering and the application of concepts from the fields of computer science,
electrical engineering and mechanical engineering to the design of robots. The focus of this
course is the interactions between a robot and the environment through sensors,
feedback and decision processes. Principles of electronic transducers, including performance,
selection, and application of sensors will be presented. Interfaces between microcontrollers and
sensors are introduced, including conditioning circuits, filters, analog-to-digital conversion,
digitization, and sampling. Basic feedback mechanisms for mechanical systems will be
implemented via electronic circuits and software mechanisms. The necessary
software concepts will be introduced for modular design and implementation of
decision algorithms and finite state machines. Laboratory sessions consist of
hands-on exercises and team projects where students design and build robots
and related sub-systems.

Recommended background: RBE 2001, ECE 2010, CS 1101 or CS 1102


Cat. I
Third of a four-course sequence introducing foundational theory and practice of
robotics engineering from the fields of computer science, electrical engineering
and mechanical engineering. The focus of this course is actuator design,
embedded computing and complex response processes. Concepts of dynamic
response as relates to vibration and motion planning will be presented. The
principles of operation and interface methods various actuators will be discussed,
including pneumatic, magnetic, piezoelectric, linear, stepper, etc. Complex
feedback mechanisms will be implemented using software executing in an
embedded system. The necessary concepts for real-time processor programming,
re-entrant code and interrupt signaling will be introduced. Laboratory sessions
will culminate in the construction of a multi-module robotic system that
exemplifies methods introduced during this course.

Recommended background: RBE 2002, ECE 2049, CS 2102, MA 2051, and
MA 2071.


Cat. I

Fourth of a four-course sequence introducing foundational theory and practice
of robotics engineering from the fields of computer science, electrical engineering
and mechanical engineering. The focus of this course is navigation, position
estimation and communications. Concepts of dead reckoning, landmark
updates, inertial sensors, and radio location will be explored. Control systems as
applied to navigation will be presented. Communication, remote control and
remote sensing for mobile robots and tele-robotic systems will be introduced.
Wireless communications including wireless networks and typical local and wide
area networking protocols will be discussed. Considerations will be discussed
regarding operation in difficult environments such as underwater, aerospace,
hazardous, etc. Laboratory sessions will be directed towards the solution of an
open-ended problem over the course of the entire term.

Recommended background: RBE 3001, ES 3011, MA 2621, or MA 2631.


This course introduces students to the social, moral, ethical, legal, and current or future philosophical issues within the context of robotic systems and related emerging technology. Students will be expected to contribute to classroom presentations, discussions and debates, and to complete a number of significant writing assignments. This course is recommended for juniors and seniors.
Recommended background: A general knowledge of robots and robotic systems.
Students may not receive credit for both RBE 3100 and RBE 310X. Recommended Background: A general knowledge of robots and robotic systems.


Cat. I

This course introduces students to the modeling and analysis of mechatronic
systems. Creation of dynamic models and analysis of model response using the bond graph modeling language are emphasized. Lecture topics include energy
storage and dissipation elements, transducers, transformers, formulation of equations for dynamic systems, time response of linear systems, and system
control through open and closed feedback loops. Computers are used extensively
for system modeling, analysis, and control. Hands-on projects will include the
reverse engineering and modeling of various physical systems. Physical models
may sometimes also be built and tested.

Recommended background: mathematics (MA 2051, MA 2071), fluids (ES 3004), thermodynamics (ES 3001), mechanics (ES 2501, ES 2503).


Cat. 1

This course focuses on the role of visual sensing in robotic manipulation. It covers fundamental manipulation concepts such as mathematical grasp formulations, grasp taxonomies, and grasp stability metrics. Various grasp planning strategies in the literature are 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 implemented. Students will integrate all of these aspects to design the whole vision-based robotic manipulation pipeline.


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.


Cat. I

This course introduces students to robotics within manufacturing systems.
Topics include: classification of robots, robot kinematics, motion generation and
transmission, end effectors, motion accuracy, sensors, robot control and automation. This course is a combination of lecture, laboratory and project
work, and utilizes industrial robots. Through the laboratory work, students will
become familiar with robotic programming (using a robotic programming
language RAPID) and the robotic teaching mode. The experimental component
of the laboratory exercise measures the motion and positioning capabilities of
robots as a function of several robotic variables and levels, and it includes the use
of experimental design techniques.
Recommended background: manufacturing (ME 1800), kinematics (ME 3310),
control (ES 3011), and computer programming.

Graduate Courses


This course introduces Biomechanics and Robotics as a unified subject addressing living and man-made

“organisms”. It draws deep connections between the natural and the synthetic, showing how the same principles

apply to both, starting from sensing, through control, to actuation. Those principles are illustrated in several

domains, including locomotion, prosthetics, and medicine. The following topics are addressed: Biological and

Artificial sensors, actuators and control, Orthotics Biomechanics and Robotics, Prosthetic Biomechanics and

Robotics: Artificial Organs and Limbs, Rehabilitation Robotics and Biomechanics: Therapy, Assistance and

Clinical Evaluation, Human-Robot Interaction and Robot Aided Living for Healthier Tomorrow, Sports, Exercise

and Games: Biomechanics and Robotics, Robot-aided Surgery, Biologically Inspired Robotics and Micro- (bio)

robotics, New Technologies and Methodologies in Medical Robotics and Biomechanics, Neural Control of

Movement and Robotics Applications, Applied Musculoskeletal Models and Human Movement Analysis. This

course meshes physics, biology, medicine and engineering and introduce students to subject that holds a

promise to be one of the most influential innovative research directions defining the 21st century.


Fundamentals of robotics engineering. Topics include forward and inverse kinematics, velocity
kinematics, introduction to dynamics and control theory, sensors, actuators, basic probabilistic
robotics concepts, fundamentals of computer vision, and robot ethics. In addition, modular
robot programming will be covered, and the concepts learned will be applied using realistic
simulators (Prerequisites: Differential
Equations (MA 2051 or equivalent), Linear
Algebra (MA 2071 or equivalent) and the ability
to program in a high-level language.)


Foundations and principles of robotic dynamics.
Topics include system
modeling including dynamical modeling of serial arm robots using Newton and Lagrange’s
techniques, dynamical modeling of mobile robots, introduction to dynamics based robot control,
as well as advanced techniques for serial arm forward kinematics, trajectory planning,
singularity and manipulability, and vision-based control. In addition, dynamic simulation techniques will be covered to apply the concepts learned using realistic simulators. An end of term team project
would allow students to apply mastery of the subject to real-world robotic platforms (Prerequisite:
RBE 500 or equivalent.)


This course demonstrates the synergy between the control theory and robotics through applications and provides an in-depth coverage of control of manipulators and mobile robots. Topics include linearization, state space modeling and
control of linear and nonlinear systems, feedback control, Lyapunov stability analysis of
nonlinear control systems, set-point control, trajectory and motion control, compliance and
force control, impedance control, adaptive robot control, robust control, and other advanced
control topics. Course projects will emphasize modeling, simulation and practical implementation of control systems for robotic applications. (Prerequisites: RBE 500 or equivalent, Linear algebra; Differential equations; Linear systems and control theory as in ECE 504 or consent of the instructor.)


Foundations and principles of parallel manipulators and legged robots. Topics include advanced spatial/3D kinematics and dynamics of parallel manipulators and legged robots including workspace analysis, inverse and forward kinematics and dynamics, motion analysis and control, and gait and stability/balance analysis of legged robots. The course will be useful for solving problems dealing with parallel manipulators as well as multi-legged robots including, but not limited to, quadruped robots, hexapod robots and any other types of multi-legged robots. A final term project allows students to show mastery of the subject by designing, analyzing, and simulating parallel and/or legged robots of their choice.

Recommended Background: RBE 500, RBE 501


Continuum robotics focuses on the study of “continuously flexible” robotic arms. This branch of robotics takes inspiration from flexible animal appendages (e.g., elephant trunks and octopus tentacles) to create manipulators capable of complex bending motions. Real-world applications of continuum robots include minimally invasive surgery, industrial inspection, and more generally any scenario that requires manipulation within highly unstructured, confined environments, where traditional rigid-link robotic arms are not suitable for use. This course introduces students to fundamental topics in continuum robot design, modeling, and control. The course culminates in the development of a continuum robot simulator, where students apply the concepts learned in the classroom. Continuum robot platforms will also be available for laboratory/experimental work. Prerequisites: RBE 501 and 502 or equivalent courses.


This course focuses on human-robot interaction and social robot learning, exploring the leading research, design principles and technical challenges we face in developing robots capable of operating in realworld human environments. The course will cover a range of multidisciplinary topics, including physical embodiment, mixedinitiative interaction, multimodal interfaces, human-robot teamwork, learning algorithms, aspects of social cognition, and long-term interaction. These topics will be pursued through independent reading, class discussion, and a final project. Units: 3 Prerequisites: Mature programming skills and at least undergraduate level knowledge of Artificial Intelligence, such as CS 4341. No hardware experience is required.) RBE 595 (Synergy of Human & Robot) and the RBE/CS 526 (HumanRobot Interaction) courses are equivalent. A student cannot take and get credit for both courses.


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. This 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 implement new design and fabrication methodologies of soft robots, read recent literature in the field, and complete a project to supplement the course material. Existing soft robotic platforms will be available for experimental work.
Prerequisites: Differential equations, linear algebra, stress analysis, kinematics, embedded programming.


This hands on course covers smart materials and actuation, with an emphasis on electroactive polymer (EAP) based materials and actuators, such as contractile EAPs, dielectric elastomers (DEAs), and ion-polymer metal composites (IPMCs). Piezoelectric materials and shape memory alloys (SMAs) are included in the course, as well as pneumatic actuation. Because smart materials and electroactivity are relatively new fields, the course involves literature reviews. Each team project will involve two different types of smart materials, where at least one smart material is electroactive. For the team projects, the class will be organized into groups, ensuring that each group had a mixture of different disciplines to promote lively discussion. Two papers will be required, one as a literature review and one about aspects of the team project. Much of the theory and applied research is yet to be done with smart materials, so this is a very creative course that implements design into the projects, which can include biomimicry. Units: 3


This course examines current issues in the computer
implementation of visual perception. Topics
include image formation, edge detection, segmentation,
shape-from-shading, motion, stereo,
texture analysis, pattern classification and object
recognition. We will discuss various representations
for visual information, including sketches
and intrinsic images. (Prerequisites: CS 534,
CS 543, CS 545, or the equivalent of one of these


Motion planning is the study of algorithms that reason about the movement of physical or virtual entities. These algorithms can be used to generate sequences of motions for many kinds of robots, robot teams, animated characters, and even molecules. This course will cover the major topics of motion planning including (but not limited to) planning for manipulation with robot arms and hands, mobile robot path planning with non-holonomic constraints, multi-robot path planning, high-dimensional sampling-based planning, and planning on constraint manifolds. Students will implement motion planning algorithms in open-source frameworks, read recent literature in the field, and complete a project that draws on the course material. The PR2 robot will be available as a platform for class projects. Physical robot platforms will be available for class projects.
Prerequisites: Undergraduate Linear Algebra, experience with 3D geometry, and significant programming experience.


This course will provide an overview of a multitude of biomedical applications of robotics. Applications covered

include: image-guided surgery, percutaneous therapy, localization, robot-assisted surgery, simulation and

augmented reality, laboratory and operating room automation, robotic rehabilitation, and socially assistive

robots. Specific subject matter includes medical imaging, coordinate systems and representations in 3D space,

robot kinematics and control, validation, haptics, teleoperation, registration, calibration, image processing,

tracking, and human-robot interaction.  Topics will be discussed in lecture format followed by interactive

discussion of related literature. The course will culminate in a team project covering one or more of the primary

course focus areas. Students cannot receive credit for this course if they have taken the Special Topics (ME

593U) version of the same course.


This project-based course integrates robotics engineering theory and practice, and provides the opportunity to apply the skills and knowledge acquired in the Robotics Engineering curriculum. The project is normally conducted in teams of two to four students. Students are encouraged to select projects with practical significance to their current and future professional responsibilities. The projects are administered, advised, and evaluated by WPI faculty as part of the learning experience, but students are also encouraged to seek mentorship from experienced colleagues in the Robotics Engineering profession. The project will include substantial analysis and/or design and conclude with a written report and a public presentation. Units: 3 Prerequisites: Since the Capstone Project will draw on knowledge obtained throughout the degree program, it is expected that students will have completed most or all of the coursework within their plan of study before undertaking the capstone project.


RBE 595 Special Topics courses are arranged by individual faculty with special expertise, these courses survey fundamentals in areas that are not covered by the regular Robotics Engineering course offerings. Courses are not always offered each semester.

RBE 595 courses offered in Fall 2022:

Haptic and Robotic Interaction (In-Person)

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.

Sensor Fusion and Perception for Autonomous Vehicles (Online)

This course focuses in Sensor Fusion, Image Processing and Computer Vision techniques for Autonomous Vehicles. The class covers four topics: Image Processing (Image Enhancement, Filtering, Advanced Edge and Texture), 2D/3D Vision (3D Geometry from Multiple view geometry, Motion Processing and Stereo) Sensor fusion (homogeneous fusion, heterogeneous fusion and sensor integration) and Image Segmentation and Object Recognition. Students will be introduced to several existing software toolboxes from Vision and Robotics, and will implement a number of smaller projects Moreover, this course presents a variety of tools and approaches for solving fundamental problems involving sensor fusion and perception. Topics to be covered include the mathematical formulation of fusion algorithms, the use of sensor fusion to solve visual perception degeneratives, time domain discrepancies, and accurate reconstruction, and the design and implementation of heterogeneous sensor fusion approaches. Prerequisite: RBE 500


This practicum provides an opportunity to put into practice the principles studied in previous courses. It will generally be conducted off campus and will involve real-world robotics engineering. Overall conduct of the practicum will be supervised by a WPI RBE faculty member; an on-site liaison will direct day-to-day activity. For a student from industry, an internship may be sponsored by his or her employer. The project must include substantial analysis and/or design related to Robotics Engineering and will conclude with a substantial written report. A public oral presentation must also be made, to both the host organization and a committee consisting of the supervising faculty member, the on-site liaison and one additional WPI faculty member. This committee will verify successful completion of the internship. (Prerequisite: Consent of practicum faculty advisor.


For M.S. or Ph.D. students wishing to gain research experience peripheral to their thesis topic, M.S. students undertaking a capstone design project, or doctoral students wishing to obtain research credit prior to admission to candidacy. For Directed Research to count as the Masters capstone requirement you must take 3 credits and include substantial analysis and/ or design and conclude with a written report and a public presentation. Units: 3 Prerequisites: Consent of an RBE affiliated research advisor


For masters students wishing to obtain research credit toward the thesis. Units: 3 Prerequisites: Consent of thesis advisor


For Ph.D. students wishing to obtain a research credit towards the dissertation. Units: 3 Prerequisites: Consent of research advisor.