Certificate
Robotics Engineering
Faculty
J. Xiao, Professor and Department Head; Ph.D., University of Michigan. Robotic manipulation and motion planning, artificial intelligence, haptics, multi-modal perception.
M. Agheli, Associate Teaching Professor; Ph.D., Worcester Polytechnic Institute (WPI), 2013. Legged Robotics
V. Aloi, Assistant Teaching Professor; Ph.D., University of Tennessee, Knoxville. Robot dynamics, robotic control, and continuum robotics.
B. Calli, Associate Professor; Ph.D., Delft University of Technology, 2015. Robotic manipulation, robot vision, machine learning, dexterous manipulation, environmental robotics.
C. Chamzas, Assistant Professor; Ph.D., William Marsh Rice University, 2023. Integrating learning and planning, planning under uncertainty, motion planning, task and motion planning, and visual task planning.
S. Faal, Assistant Teaching Professor; Ph.D., Worcester Polytechnic Institute, 2018. Optimization and mechatronics, optimal and nonlinear control, state estimation, and motion planning.
L. Fichera, Assistant Professor; Ph.D., University of Genoa/Italian Institute of Technology. Continuum robotics, medical robotics, surgical robotics, image-guided surgery, laser-based surgery, medical devices
G. S. Fischer, Professor; Ph.D., Johns Hopkins University. Medical cyber-physical systems, surgical robotics, image-guided interventions, assistive technology, robot modeling and control, automation, sensors and actuators.
K. Leahy, Assistant Professor; Ph.D., Boston University, 2017. Robotics, formal methods, multi-agent systems, and artificial intelligence.
G. C. Lewin, Associate Teaching Professor and Robotics Engineering Associate Head; Ph.D., University of Virginia, 2003. Systems integration, mobile robotics, mechatronics, sensors and control.
G. Li, Assistant Professor; Ph.D., New York University, 2023. Dynamics, planning and control of robotic systems, aerial robotics, multi-robot system, and human-robot interaction.
Z. Li, Assistant Professor; Ph.D., University of California, Santa Cruz, 2014. Human-robot interaction and interface, human-robot mutual adaptation, remote robot manipulation, nursing assistant robots.
W. R. Michalson, Professor; Ph.D., Worcester Polytechnic Institute. Satellite navigation, real-time embedded computer systems, digital music and audio signal processing, simulation and system modeling.
C. D. Onal, Associate Professor; Ph.D. Carnegie Mellon University, 2009. Soft robotics, printable robotics, origami-inspired robotics, bio-inspiration, control theory, human augmentation.
C. Pinciroli, Associate Professor; Ph.D., Université Libre de Bruxelles, Belgium, 2014. Swarm robotics, multi-agent systems, software engineering, programming languages, human-swarm interaction.
G. Pittiglio, Assistant Professor; Ph.D., University of Leeds, 2022. Medical robotics, continuum robotics, robot control and sensing.
A. Rosendo, Assistant Professor; Ph.D., Osaka University, 2014. Mobile robotics, mechatronics, machine learning.
N. Sanket, Assistant Professor; Ph.D., University of Maryland, College Park, 2021. Robot perception, artificial intelligence, aerial robotics, computer vision, sensor fusion, SWAP-Aware minimalist autonomy.
F. Yuan, Assistant Professor; Ph.D., University of Tennessee, Knoxville, 2024. Socially assistive robotics (SAR), multimodal human-robot interaction, human-centered embodied AI, robot-assisted dementia care, technology-assisted healthcare.
H. Zhang, Associate Professor; Ph.D., Johns Hopkins University, 2017. Biomedical robotics, biomedical imaging, ultrasound and photoacoustic instrumentation, functional imaging of brain and cancer, image-guided therapy and intervention.
Associated Faculty
E.O. Agu, Associate Professor; Ph.D., Massachusetts, 2001. Computer graphics, wireless networking, mobile computing and mobile health.
S. Barton, Associate Professor; Ph.D. University of Virginia, 2012. Human-robot interaction in music composition and performance, design of robotic musical instruments, music perception and cognition, audio production.
C. A. Brown, Professor; Ph.D., University of Vermont, 1983. Surface metrology, machining, grinding, mechanics of skiing, axiomatic design.
S. Farzan, Associate Professor, Ph.D., Georgia Institute of Technology, 2021. Safety-critical motion planning and control of cyber-physical systems.
C. Furlong, Professor; Ph.D., Worcester Polytechnic Institute. MEMS and MOEMS, micro-/nano-technology & -fabrication, mechatronics, laser metrology & applications, holographic and ultrasonic imaging and NDT, computer modeling of dynamic systems, acoustics.
G.R. Gaudette, William Smith Dean’s Professor of BME; Ph.D. SUNY Stony Brook; Cardiac biomechanics, myocardial regeneration, biomaterial scaffolds, tissue engineering, stem cell applications, optical imaging techniques, cellular agriculture, crossing biological kingdoms.
X. Huang, Professor; Ph.D., Virginia Tech. Reconfigurable computing, VLSI integrated circuits, networked embedded systems
D. Korkin, Associate Professor; PhD., University of Illinois, Chicago, IL 2014. Data mining, social networks, machine learning, big data analytics.
Y.S. Liu, Assistant Professor; Ph.D. University of Maryland, 2011. Fiber optical tweezers, silicon nanophotonics and nanomechanics, optofluidics, fiber optic sensors, cell mechanics, biomimetics.
M. Nemitz, Assistant Professor; Ph.D. University of Edinburgh, 2018. Robotic soft materials, magnetism, fluidics, machine learning.
P. Radhakrishnan, Assistant Teaching Professor; PhD., The University of Texas at Austin, 2014. Automated design and manufacturing; entertainment and medical engineering; optimization, machine learning and software development; kinematics, dynamics and design education.
C. L. Sidner, Research Professor; Ph.D., Massachusetts Institute of Technology, 1979. Discourse processing, collaboration, human-robot interaction, intelligent user interfaces, natural language processing, artificial intelligence.
J.Skorinko, Professor; Ph.D. University of Virginia, 2007. Social psychology, decision-making, interpersonal interactions.
E. Solovey, Assistant Professor; Ph.D., Tufts University, 2012. Human-computer interaction, user interface design, novel interaction modalities, human-autonomy collaboration, machine learning.
Y. D. Telliel, Assistant Professor; Ph.D., City University of New York-Graduate Center, 2017. Anthropology of robots and roboticists, AI ethics, public interest robotics, qualitative research methods in human-computer interaction, engineering education.
A. M. Wyglinski, Professor; Ph.D., McGill University. Wireless communication systems engineering, vehicular technology, cognitive radio, software-defined radio, autonomous vehicles, wireless spectrum, vehicular security, cyber-physical systems.
Z. Zhang, Assistant Professor; Ph.D., Oxford Brookes University, 2013. Computer vision and machine learning, object recognition/detection, data-efficient learning, with applications; deep learning, optimization.
Y. Zheng, Assistant Professor; PhD., University of Michigan, 2016. Advanced and biomedical manufacturing, medical device design, tissue mechanics, biomedical machining process and modeling, catheter-based surgical devices, medical simulation, vascular ultrasound imaging, abrasive machining processes for biomedical and ceramic materials.
Program of Study
M.S. Program
The Robotics Engineering Department offers the M.S. degree with thesis and non-thesis (coursework only) options. The department strives to educate students to:
- Have a solid understanding of the fundamentals of Robotics Science, Engineering, and Systems.
- Have an awareness of the management and systems contexts within which robotic systems are engineered.
- Develop advanced knowledge in selected areas of robotics, culminating in a capstone research or design experience.
Admission Requirements
Students will be eligible for admission to the program if they have earned an undergraduate degree in Computer Engineering, Computer Science, Electrical Engineering, Mechanical Engineering or a related field from an accredited university consistent with the WPI graduate catalog. Admission will also be open to qualified WPI students who opt for a five-year Bachelors-Masters program, with the undergraduate major in Computer Science, Electrical & Computer Engineering, Mechanical Engineering, Robotics Engineering or a related field. Admission decisions will be made by the Robotics Engineering Graduate Program Committee based on all of the factors presented in the application.
Robotics Engineering Laboratories
Adaptive and Intelligent Robotics (AIR) Lab
Professor Jing Xiao
The AIR Lab is located at 301 (3rd floor) of 85 Prescott Street. Research at the AIR Lab is focused on robotic systems that can best adapt to unknowns, uncertainties, and changes in the working environments, through real-time perception, planning, learning, and execution in seamless synergy. Interested areas include robotic assembly, manipulation, and navigation in human-centered environments, with different kinds of manipulators, from articulated to continuum/soft robots, and in a wide range of applications, including assembly, additive manufacturing, material handling, maintenance and repair, medical and healthcare, manufacturing, and services. Further information is available at https://wp.wpi.edu/airlab/home/.
Automata Lab
Professor Kevin Leahy
The Automata Lab focuses on developing the next generation of autonomous robotic technology, with an emphasis on formal methods, machine learning (ML), planning, and control. Our work strives to create intelligent mobile robots capable of independent decision-making backed by the strong guarantees provided by formal methods. We envision robots that can act on high-level input from a human operator, correctly and reliably, while interacting with objects, people, and other agents in real-world environments. Our work falls under two primary thrusts: 1) Applying tools from formal methods to real-world systems. This entails extending methods from automata- and optimization-based planning from specifications to include real-world uncertainties. 2) Applying formal methods to ML methods for planning and control. This work includes providing guarantees on ML-based control and estimation, especially in terms of safety guarantees. More information is available at https://wp.wpi.edu/automata/.
Automation and lnterventional Medicine (AIM) Lab
Professor Gregory Fischer
The Automation and lnterventional Medicine Laboratory Robotics Research Laboratory (AIM Lab) is located at Gateway Park. The primary focus of projects in the AIM Lab is medical robotics including: robotic surgery, image-guided surgery, MRI-compatible mechatronics, rehabilitation robotics, socially assistive robotics, and biofabrication. The lab contains student workstations, equipment for mechanical and electrical design, construction, configuration, and testing of robots, control systems, and automated test fixtures, including state-of-the-art electronics testing and micro-electronics assembly equipment and supplies. An optical tracker is available for motion capture. The lab houses MRI robot controllers developed in the AIM Lab and custom control electronics for high precision control of piezoelectric motor drive waveforms and corresponding robotic system testbeds. A da Vinci Research Kit (dVRK) surgical robot is also available in the lab which includes the Intuitive Surgical robot with custom open control systems. Access to medical imaging in a clinical site is available through collaboration with the nearby UMass Medical School and with the Brigham and Women's Hospital. The research in the AIM Lab is directed by Prof. G. Fischer. Further information can be found at http://aimlab.wpi.edu/.
Cognitive Medical Technology (COMET) and Robotics Laboratory
Professor Loris Fichera
Research in the COMET Laboratory focuses on the development of smart medical devices and robots. Specific focus areas include autonomous and semi-autonomous surgical robotics, continuum (continuously flexible) surgical instruments and image-guided surgery. The lab features state-of-the-art experimental equipment, including two surgical laser systems (10,600 and 532 nm), an NDI Aurora electromagnetic tracker, a FLIR A655sc thermal camera and a Franka Emika 7-DoF Panda manipulator. The lab has research collaborations with clinical partners at Brigham and Women's Hospital (Boston, MA), Vanderbilt University Medical Center (Nashville, TN) and the Children's National Hospital (Washington, D.C.). The lab is located in room 4832 at 50 Prescott St. and is directed by Prof. L. Fichera. Further information is available at https://comet-lab.github.io/.
Efficient Learning and Planning for Intelligent Systems (ELPIS) Lab
Professor Constantinos Chamzas
The Elpis Lab has a broad interest in autonomous robotic system capable of reasoning about and interacting with the physical world. The primary goal is to develop agents that are efficient, robust, and capable of learning from real-world interactions. Current research projects focus on the integration of classical planning algorithms and state-of-the-art machine learning techniques, aiming to advance 1) planning efficiency, 2) planning robustness, and 3) planning from visual inputs. Further information can be found at www.elpislab.org.
Human-inspired Robotics (HIRO) Lab
Professor Zhi Li
Research at the HiRo Lab aims to develop shared autonomous human-robot interfaces to enable humans to effectively control and supervise remote robot manipulation, with a focus on the applications of nursing, living and manufacturing assistance robots. We are interested in shared autonomous robot manipulation control, adaptive human-robot interaction and interfaces, and augmented reality and multimodal human-robot communication. We also develop novel approaches for developing the knowledge and skills of the nursing workforce to control and collaborate robots in patient care. We collaborate with experts in social science, nursing practice and education to advance nursing robot technologies and mitigate technology barriers and bias for users of diverse age and gender. Further information is available at https://labs.wpi.edu/hiro/.
Manipulation and Environmental Robotics (MER) Lab
Professor Berk Calli
The Manipulation and Environmental Robotics Lab primarily focuses on enhancing manipulation capabilities of robots. The research integrates visual feedback, advanced control methods, active vision framework, machine learning algorithms and intelligent mechanical design to achieve robust and dexterous robotic systems. Such systems are essential for executing grasping and manipulation tasks in unstructured environments, including homes, offices, modern warehouses, and collaborative manufacturing stations. One of the main themes of the lab is environmental robotics, i.e. utilizing robots to solve environmental problems such as waste management issues and recycling efficiency. The lab is directed by Prof Berk Calli. Further information is available at https://wp.wpi.edu/merlab/.
Medical Frontier Ultrasound Imaging and Robotic Instrumentation (FUSION) Lab
Professor Haichong (Kai) Zhang
Medical FUSION (Frontier Ultrasound Imaging and Robotic Instrumentation) Lab focuses on the interface of medical robotics, sensing, and imaging, and to develop robotic assisted imaging systems as well as image-guided robotic interventional platforms, where ultrasound and photoacoustic (PA) imaging are two key modalities to be investigated and integrated with robotics. The scope of innovation focuses on medical robotics, sensing and imaging for (1) co-robotic imaging, where a robotic component is essential to reduce user-dependency in ultrasound scanning, to build an image with higher resolution and contrast, and to miniaturize and simplify imaging platform and (2) PA-based functional image-guided interventions that give additional information for surgical guidance with high sensitivity and specificity. Further, we will also tackle (3) mathematical and algorithmic challenges behind computer assisted interventions such as hand-eye calibration to support these deployments. The developed systems will synergistically improve both image quality and surgical accuracy and specificity towards diagnostic and interventional applications. https://medicalfusionlab.wordpress.com/.
Novel Engineering of Swarm Technologies (NEST) Lab
Professor Carlo Pinciroli
The Novel Engineering for Swarm Technologies (NEST) Laboratory focuses on the design of algorithms and software tools for swarm robotics and multi-agent systems, with applications to disaster recovery, firefighting, and space applications. The lab offers a swarm of 10 Khepera IV robots (along with extension modules such as grippers and LIDARs), 100 Kilobots, and 16 AWS DeepRacers. In addition, the lab has a dedicated experimentation area equipped with a Vicon motion capture system comprising 10 cameras (2.2 Megapixel resolution at 330 frames per second, with varifocal lenses and an IR strobe), a dedicated 1 Gb network connected to a workstation through Vicon Lock+, and the latest version of Vicon image analysis software (Vicon Nexus Standalone, Vicon BodyBuilder, Tracker 3.0 Standalone). Further information is available at https://www.nestlab.net.
Perception and Autonomous Robotics (PeAR)
Professor Nitin Sanket
The Perception and Autonomous Robotics (PeAR) Group works on bio-inspired perception for enhancing robot autonomy at scales not thought possible before. We focus on building task-driven and parsimonious Artificial Intelligence frameworks using onboard sensing and computation. Our work can be categorized into three sub-domains with the common goal of building better autonomy under severe resource constraints (Size, Weight, Area and Power): (1) Active and Interactive Perception (Using action/interaction to gather more information), (2) Novel Perception (inception of new mathematical formulations based on data statistics obtained from various modalities such as uncertainties in optical flow) and (3) Novel Sensing (creating custom sensing mechanisms such as custom apertures to enable better task-driven perception). We develop mathematical tools and deploy them on real robots such as hummingbird-sized aerial robots and smaller ground robots. Further information is available at pear.wpi.edu.
PracticePoint
Professor Gregory Fischer
PracticePoint is a Massachusetts Technology Collaborative (Mass Tech) supported R&D center that seeks to improve healthcare technologies and develop new medical cyber-physical systems. PracticePoint provides an agile and scalable, collaborative research facility empowering public and private universities, research institutions, industry and innovators to incorporate cyber-physical systems into medical devices and equipment that will improve performance, security, accuracy, timeliness, costs and outcomes in human healthcare. PracticePoint fosters collaborations among its affiliates through state-of-the-art clinical care test beds, secured project pods, collaboration suites and shared tool bays. The point-of-practice environments including: medical imaging coupled with a hybrid operating room suite (including an MRI scanner), a controlled care environment (reconfigurable as ICU, exam room, and recovery room), rehabilitative care suites (including motion capture and rehab equipment), and a residential setting (highly instrumented mock home environment). These point-of-practice care suites are co-located we will have advanced manufacturing (including CNC machining, 3D printing, laser cutting), electronics assembly and test equipment, and build areas. The facility also comprises office spaces for faculty and graduate students, individual research group lab spaces, and reconfigurable "lab pods." Further information can be found at http://practicepoint.org.
WPI Robot Communications and Navigation Laboratory
Professor William Michalson
The Robot Communications and Navigation Laboratory at 85 Prescott Street conducts research into the navigation of and communications with air, land and sea robots in indoor and outdoor locations. The laboratory has platforms for land robots as well as several rotorcraft and sailing platforms and has competed in intelligent ground vehicle and sea vehicle competitions.
WPI Soft Robotics Laboratory
Professor Cagdas Onal
The Soft Robotics Laboratory is located in HL 127, and supports personnel and equipment required for the design, development, and control of next-generation soft, flexible, and semi-rigid robotic systems. Projects in the lab include studying and developing soft robotic snakes, octopus arms, origami-inspired hexapods, tentacles, flying robots, wearable haptic interfaces, human-robot interaction, and multi-robot systems.
Equipment in the Soft Robotics lab includes tools for design, fabrication, experimentation, and analysis, including an Epilog Zing 24 CO2 laser cutter, a dual nozzle 3D printer, a motion capture area, various semi-rigidware packages for mechanical and electronic design, a full custom-made flexible circuit fabrication and assembly equipment suite, a large-workspace optical microscope, an elastomeric fabrication workbench, and various data acquisition and analysis systems. The lab currently supports research activities in elastomeric robotic systems, printed circuit and sensor manufacturing, origami-inspired foldable systems, assistive soft robotic monitoring, bio-inspired stereo vision, and prosthetic robotics. Research in the Soft Robotics Laboratory is directed by Prof Onal. Further information can be found at http://softrobotics.wpi.edu/.
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Certificate in Robotics Engineering, -
M.S. in Robotics Engineering, Master of Science -
Ph.D. in Robotics Engineering, Ph.D.
Classes
BME 520/RBE 520: Biomechanics and Robotics
Department
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
BME 580/RBE 580: Biomedical Robotics
Department
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.
Prerequisites
Linear algebra, ME/RBE 301 or equivalent.
CS 526/RBE 526: Human-Robot Interaction
Department
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 real-world human environments. The course will cover a range of multidisciplinary topics, including physical embodiment, mixed-initiative interaction, multi-modal 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.
Prerequisites
Mature programming skills and at least undergraduate level knowledge of Artificial Intelligence, such as CS 4341. No hardware experience is required
CS 549/RBE 549: Computer Vision
Department
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
RBE 500/ ME 527: Foundations of Robotics
Department
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
RBE 501/ME 528: Robot Dynamics
Department
Foundations and principles of robot 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.
Prerequisites
RBE 500 or equivalent
RBE 501/ME 528: Robot Dynamics
Department
Foundations and principles of robot 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.
Prerequisites
RBE 500 or equivalent
RBE 502: Robot Control
Department
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 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
RBE 510: Multi-Robot Systems
Department
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. Students cannot receive credit for this course if they have taken the Special Topics (ME 593S) version of the same course.
Prerequisites
Linear algebra, differential equations, linear systems, controls, and mature programming skills, or consent of the instructor.
RBE 510: Multi-Robot Systems
Department
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.
RBE 511: Swarm Intelligence
Department
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 decisionmaking. 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 through a final project in swarm robotics.
Recommended Background
C++/Python/Matlab programming; Linear algebra; Probability and statistics; Calculus.
RBE 521: Legged Robotics
Department
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 300, RBE 501.
RBE 521: Legged Robotics
Department
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 300, RBE 301.
RBE 522: Continuum Robotics
Department
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 manipulato rs 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 RBE 502, or equivalent courses.
RBE 526/CS 526: Human-Robot Interaction
Department
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 real-world human environments. The course will cover a range of multidisciplinary topics, including physical embodiment, mixed-initiative interaction, multi-modal 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.
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 (Human-Robot Interaction) courses are equivalent. A student cannot take and get credit for both courses.
RBE 530: Soft Robotics
Department
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.
RBE 530: Soft Robotics
Department
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.
RBE 533: Smart Materials & Actuation
Department
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.
RBE 535: Printable Robotics
Department
This graduate-level course provides an in-depth examination of 3D printing technologies tailored for the creation of fluidically-driven robotic systems with a focus on design, fabrication, modeling, and control mechanisms. The curriculum encompasses a range of topics, such as fused deposition modeling using thermoplastic polyurethanes, advanced multi-material printing techniques, the engineering of impermeable material systems, and the design of fluidic actuators. The course also covers the fabrication of printable fluidic transistors, the integration of volatile and non-volatile memory elements, and the development of both combinational and sequential fluidic logic circuits, including fluidic state machines. Instruction in COMSOL multi-physics simulation will equip students to correlate empirical observations with numerical data. The course structure includes weekly lectures complemented by hands-on laboratory assignments, where student groups will gain practical experience using cost-effective FDM printers. The course is particularly well-suited for students seeking to deepen their understanding of 3D printing, those interested in constructing their own robotic systems, or individuals aiming to conduct research in the fields of soft robotics, robotic materials, or printable robotics.
RBE 544: Imaging for Medical Robotics
Department
This course aims to introduce the physical principles behind modern medical imaging, including radiography, X-ray computed tomography, nuclear medicine, ultrasound imaging, and magnetic resonance imaging, and their adaptation for image-guided interventions. In robotics, vision and perception play a crucial role, but the optical camera provides only surface information, which limits its usefulness in medical robotics for surgical guidance and diagnosis. To perceive the structural and functional information inside the body, medical imaging is a critical component. Topics include mathematical and physical foundations of each modality, including their interactions with biological tissue. Additionally, the course will present advanced imaging solutions that combine with robotic instrumentation to enable robotic-assisted imaging and image-guided robotic interventions. In the team project, students will tackle real clinical challenges using novel imaging and instrumentation methods.
Recommended Background
Linear algebra, Basic skills in programming
RBE 549/CS 549: Computer Vision
Department
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
RBE 550: Motion Planning
Department
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.
RBE 575: Safety and Guarantees for Autonomous Robots
Department
Robotic and AI systems have strong potential to directly impact our well-being, from self-driving cars to medical robots. Therefore, it is important to consider strong guarantees on the correctness and safety of their behavior. These guarantees ensure the robot will execute the desired behavior and will not execute undesired behavior. The course will define formal notions of system properties such as safety and liveness, explain how to model and analyze those properties in systems that make decisions and act on them, and understand the specific challenges related to making guarantees on embodied AI systems. This course will cover many topics related to formal guarantees of safety and correctness in robotic and AI systems, including temporal logic-based planning, safe control via invariants and control barrier functions, neural net verification, closed loop control with machine learning components, safe reinforcement learning, and other state-ofthe-art topics at the intersection of safety, guarantees, AI, and robotics.
Prerequisites
RBE 500
Recommended Background
Machine Learning or Intro to AI.
RBE 577: Machine Learning for Robotics
Department
This graduate-level course delves into the intersection of machine learning and robotics. The curriculum will explore the integration of contemporary learning techniques in robotic areas such as manipulation, navigation, planning, control, decision-making, and other pertinent challenges in robotics. Advanced deep learning techniques and their applications in robotics will be covered, including supervised learning (e.g., behavioral cloning, state prediction), reinforcement learning (e.g., actor-critic, visual foresight), and unsupervised/self-supervised methods (e.g., world model construction, learning forward dynamic models). In addition, the generalizability of these methods will be discussed, recent, and experimental studies will be conducted, examining the challenges of applying these techniques on physical systems.
Prerequisites
RBE 500 or equivalent
Recommended Background
RBE 501 and RBE 502.
RBE 580: Biomedical Robotics
Department
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 301 or equivalent Students cannot receive credit for this course if they have taken the Special Topics (ME 593U) version of the same course.
RBE 593: Directed Research for Capstone Experience
This course is for M.S. students who plan to use Directed Research to satisfy their Capstone Experience requirement. To count for the Capstone Experience requirement, the project must be approved by the project advisor at the start of the semester. The project advisor must be affiliated with Robotics Engineering. The project must include substantial analysis and/or design and conclude with a written report and a public presentation.
Prerequisites
Consent of an RBE affiliated research advisor. Before Students can enroll in RBE 593, they must have completed the 9-credit RBE Foundations and additional 6 credits including any combination of RBE core, electives, and Engineering Context.
RBE 594: Capstone Project Experience in Robotics Engineering
Department
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.
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
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.
Prerequisites
Consent of instructor
RBE 596: Robotics Engineering Practicum
Department
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, a practicum 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. There can be no confidential or proprietary company information in the project. 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 practicum.
Prerequisites
Consent of practicum faculty advisor. The student must have completed the 9-credit RBE Foundations and additional 6 credits including any combination of RBE core, electives, and Engineering Context before enrolling in RBE 596.
RBE 597: Independent Study
Department
Approved study of a special subject or topics selected by the student to meet his or her particular requirements or interests.
Prerequisites
B.S. in CS, ECE, ME, RBE or equivalent and consent of advisor
RBE 598: Directed Research
Department
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 for the Master's capstone experience requirement, the student must enroll in 3 credits for the chosen semester and the project must be approved by the project advisor at the start of the semester. The project advisor must be affiliated with Robotics Engineering. The project must include substantial analysis and/or design and conclude with a written report and a public presentation.
*Starting Fall 2024, M.S. students looking to do the capstone experience should register for RBE 593
Prerequisites
Consent of an RBE affiliated research advisor
RBE 599: Thesis Research
Department
For masters students wishing to obtain research credit toward the thesis.
Prerequisites
Consent of thesis advisor
RBE 699: Dissertation Research
Department
For Ph.D. students wishing to obtain a research credit towards the dissertation.
Prerequisites
Consent of research advisor.