Residential Price: $4,998 USD (+ supplemental fees)
Extended Commuter Price: $3,398 USD (+ supplemental fees)
Commuter Price: $2,798 USD (+ supplemental fees)
Late June: June 27-July 9, 2021
Early July: July 11-July 23, 2021
Late July: July 25-August 6, 2021
Throughout the two-week summer AI & robotics program, students will work on several laboratory-based projects that focus on each subsection of the broad field of robotics. These projects will provide experience with system modeling, real-time control, embedded software, and more. At the end of the course, students will design and fabricate a working robotics system in a group-based course project which will combine all elements of the lab projects.
With the close proximity of MIT, Boston has become a hot spot for robot tech. There are numerous robotics companies in Boston that have a hand in innovating everything from agriculture to surgery. Excursions may include research facilities on the forefront of the latest technology innovations in Boston.
Meet your instructor
John Keszler, Ph.D. candidate
John Keszler is a PhD candidate in Electrical Engineering and Computer Sciences (EECS) at Massachusetts Institute of Technology. John was part of the Harvard Undergraduate Robotics Club and led a team of 20 people to design and build the next generation of Mars rovers that will one day work alongside astronauts exploring the Red Planet. He has also implemented machine learning techniques for autonomous soccer playing robots. John graduated from Harvard University cum laude in S.B Electrical Engineering with Honors.
Topics you'll explore
Circuit design and debugging: How can you design a line sensor for a robot? How can we optimize our power delivery systems to extend our robot’s battery? How does a circuit design change from prototype to production?
Embedded programming: How do you program a robot to do what you want? Understanding the differences and optimizations needed for embedded systems.
Mechanical Design: How to design, fabricate and optimize a part using CAD tools.
Sensors and actuators: How robots perceive their environment? What sensors would they use? And how would they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complicated tasks?
Data science: How we can observe and collect data using robotic systems, and then use modeling and optimization tools to improve our robots.