Engineering Master on Applied Artificial Intelligence

Snapshots of the Robotics course (video lectures) for the Engineering Master program on Applied Artificial Intelligence, in partnership with Swiss Distance Learning University.


Week 1: Introduction
  • Why AI in robotics is hard?
  • A brief history of robotics and autonomous machines
  • Learning from demonstration (observational learning, kinesthetic teaching, correspondence problems)
Week 2: Tools for AI in robotics
  • Simulators and visualizers
  • ROS middleware
  • Toolkits and softwares
Week 3: Movement primitives I
  • Movements as superposition of basis functions
  • Bézier curves and Bernstein polynomials
Week 4: Movement primitives II
  • Locally weighted regression
  • Gaussian mixture regression
  • Dynamical movement primitives
Week 5: Operational space control
  • Forward kinematics
  • Inverse kinematics
  • Task prioritization and nullspace control
Week 6: Human-robot collaboration
  • Linear dynamical systems
  • Gravity compensation
  • Impedance control
Week 7: Anticipation and planning
  • Linear quadratic regulation (LQR)
  • Linear quadratic tracking (LQT)
  • Iterative LQR (iLQR)
Week 8: Ergodic control
  • Exploration behaviors
  • Decomposition as Fourier series
  • Spatial coverage problems
Week 9: Manifolds in robotics
  • Representations of orientation data
  • Quaternions
  • Riemmanian geometry