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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).


  • 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 sub-systems. 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.


  • Cat.I 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. 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


  • 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.)


  • This course covers the foundation and principles of multi-robot systems. The course will cover the development of the field and provide an overview on different control architectures (deliberative, reactive, behavior-based and hybrid control), control topologies, and system configurations (cellular automata, modular robotic systems, mobile sensor networks, swarms, heterogeneous systems). Topics may include, but are not limited to, multi-robot control and connectivity, path planning and localization, sensor fusion and robot informatics, task-level control, and robot software system design and implementation. These topics will be pursued through independent reading, class discussion, and a course project. The course will culminate in a group project focusing on a collaborative/cooperative multi-robot system. The project may be completed through simulation or hands-on experience with available robotic platforms. Groups will present their work and complete two professional-quality papers in IEEE format. (Prerequisites: Linear algebra, differential equations, linear systems, controls, and mature programming skills, or consent of the instructor.) Students cannot receive credit for this course if they have taken the Special Topics (ME 593S) version of the same course.


  • 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. Recommended background: foundation of physics, linear algebra and differential equations; basic programming skills e.g. using MATLAB, undergraduate level biomechanics, robotics.


  • 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


  • 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 courses.)


  • 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. Recommended background: Linear algebra, ME/ RBE 501 or equivalent. 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


  • Arranged by individual faculty with special expertise, these courses survey fundamentals in areas that are not covered by the regular Robotics Engineering course offerings. Exact course descriptions are disseminated by the Robotics Engineering Program well in advance of the offering. Units: 3 Prerequisites: Consent of instructor


  • 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.