Courses

Course Title: Robotics

Program

Bachelor AI - Minor Situated AI (required course)

Description

The first half of the course will cover the basics of robotics and robot control. Topics will include forward/inverse kinematics, modeling of robots, Denavit-Hartenberg, the basics of dynamics, trajectory planning and optimization and control algorithms. In the second half, the course will give an introduction into robot learning and embodied AI, i.e., the combination of artificial intelligence & machine learning with robotics. This will include the discussion of model-free and model-learning approaches for robotic tasks, their disadvantages and advantages over control methods.

Literature

Recommended to supplement the course:

• Mark W. Spong, Seth Hutchinson, M. Vidyasagar, “Robot Modeling and Control”
• Wiley B. Siciliano, L. Sciavicco. Robotics: Modelling, Planning and Control, Springer
• C.M. Bishop. Pattern Recognition and Machine Learning, Springer
• R. Sutton, A. Barto. Reinforcement Learning - An Introduction, MIT Press

Course Title: Learning Machines

Program

Master AI (elective course)

Description

This course concerns designing robot systemsthat can sense their environment, act upon it, and improve their behavior to solve tasks. Most of the course encompasses practical sessions with only a few lectures. Multiple tasks should be delivered, and students have the freedom to choose different learning and optimization methods to work with. All tasks will be done in simulation AND in hardware using Robobo robots. The Robobos are wheeled robots with fixed bodies that possess infrared sensors and a camera. Students are expected to design/optimize the controllers (brains) of the robots to solve the due tasks while utilizing the sensors as inputs. For the simulations, a robot programming/simulation framework will be provided to students. The simulator utilized is VREP (Coppelia).