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.

Syllabus

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