Baxter Robot Revival by Deep Learning Application
|Study location||Latvia, Riga|
|Type||Short-duration course, full-time|
|Nominal duration||2 weeks; 17 July–28 July 2023 (6 ECTS)|
|Awards||(Certificate on Completion)|
|Tuition fee||€800 per programme|
Participants must be enrolled in a degree related to computer science, IT, engineering, cybersecurity, electronics, programming, etc.
The entry qualification documents are accepted in the following languages: English.
Often you can get a suitable transcript from your school. If this is not the case, you will need official translations along with verified copies of the original.
Applications are accepted from the following territories (based on citizenship): Americas, Central Asia, Eastern Asia, Europe, Indonesia, Oceania, Vietnam, Western Asia.
A motivation letter must be added to your application.
It seems that no one is surprised that we encounter robots more and more in our everyday life. To a greater or lesser extent, each of us is already used to the coexistence of humans and robots. We can say that the future that was reflected in science fiction books and films has arrived. Nowadays another question has become relevant: can we imagine that our colleague at work can also be a robot?
The summer school ‘Making a Baxter robot alive using deep learning’ will bring human-robot interaction into the spotlight. Participants in the summer school will have a unique opportunity to work with the Baxter robot by programming specific tasks for it, which will allow a deeper understanding of the capabilities of the mechanical ‘colleague’. During school, participants will be introduced to a deep understanding of mathematics, theory, and practice behind deep machine learning, which will be used to control the Baxter robot.
It is important to mention that the Baxter robot was not chosen at random, as this specific machine was created to be able to collaborate and develop more successful human-robot co-working possibilities. Baxter as a humanoid robot can handle a variety of repetitive and precision tasks that humans perform, and therefore it is very popular among various industries.
The program includes workshops and lectures in:
Calculus, linear algebra, and probability theory in practical applications;
Practical applications of Python, Numpy, and PyTorch;
Application of artificial neural networks for vision tasks, such as classification and segmentation;
Reinforcement learning in simulation and physical Baxter robot environment;
Baxter robot programming to execute mobile robot tasks.