Using Computer Vision for Object and Face Detection with Go1
Course Curriculum
-
Preview01 - Tune Color Range Demo (7:46)
-
Start02 - Tune Color Range Explanation (2:50)
-
Start03 - Object Detection (Bark For Treat) Demo (2:58)
-
Start04 - Object Detection (Bark For Treat) Explanation (5:30)
-
Start05 - Tune Color Range Real Time Demo (8:54)
-
Start06 - Object Detection (Bark For Treat) Real Time Demo (3:02)
-
Start07 - Real Time Code Explanation (2:41)
DroneBlocks now includes its comprehensive curriculum and courses on quadruped robots for FREE for one-year with every purchase of a Unitree Go2 EDU robot, opening the door to advanced robotics education for educators, students, and hobbyists alike. This initiative provides immediate access to high-quality learning materials, ensuring that learners can dive into the world of robotics with the best tools and knowledge at their fingertips. Learn more about the Quadruped Robotics Program from DroneBlocks Here.
Explore the cutting-edge realm of computer vision with the Unitree Go1 Quadruped Robot Dog in this dynamic course authored by Gabriel Margolis, a distinguished graduate student from the prestigious MIT Robotics Lab. This comprehensive series of lessons delves into the intricacies of object and face detection, leveraging the remarkable capabilities of the Unitree Go1 robot dog.
Throughout the course, you'll gain hands-on experience in harnessing computer vision algorithms to empower the robot dog's perception and interaction skills. From understanding the fundamentals of computer vision to implementing advanced techniques, Gabriel Margolis will expertly guide you through each step of the process. By the end of this course, you'll be equipped with the knowledge and practical skills to propel the Unitree Go1 quadruped robot dog into a new era of cognitive ability. Embark on this transformative learning journey and unlock the potential of computer vision with a true MIT robotics visionary.
Your Instructor
Graduate Researcher | Robotics Enthusiast
Gabriel Margolis is a passionate graduate researcher at the MIT Robotics Lab, specializing in the realm of quadruped robots and their dynamic applications. With a strong foundation in robotics and a keen focus on innovation, Gabriel has been instrumental in pushing the boundaries of what quadruped robots can achieve.
Drawing from his extensive academic journey, Gabriel's expertise encompasses the intricate interplay between mechanical design, software development, and advanced control systems. His profound understanding of quadruped locomotion mechanics has led to groundbreaking contributions in enhancing the mobility and agility of these remarkable machines.
Gabriel's commitment to excellence is vividly showcased through his Github repository (https://gmargo11.github.io/), where he shares his pioneering work in the field. His GitHub presence serves as a testament to his dedication, offering insights into his research, projects, and collaborative endeavors that revolve around quadruped robots.
Driven by an insatiable curiosity, Gabriel Margolis is not only shaping the future of quadruped robotics but also inspiring the next generation of roboticists. His exceptional journey through the MIT Robotics Lab has equipped him with the skills, knowledge, and passion to revolutionize the capabilities of quadruped robots, unlocking their potential to tackle diverse challenges across industries.