Probabilistic Learning for Robotics (RO5601) WS18/19

[WS2018/19] In the winter semester, I will teach a course on Probabilistic Learning for Robotics which covers advanced topics including graphical models, factor graphs, probabilistic inference for decision making and planning, and computational models for inference in neuroscience. The lecture will take place in the Seminarraum Informatik 5 (Von Neumann) 2.132 from 12.00 – 14.00 on selected Thursdays.

In accompanying exercises and hands on tutorials the students will experiment with state of the art machine learning methods and robotic simulation tools. In particular, Mathworks’ MATLAB,  the robot middleware ROS and the simulation tool V-Rep will be used. The exercises and tutorials will also take place in the seminar room  2.132 on selected Fridays (see the course materials and dates below).

Prerequisites (recommended) 

  • Humanoid Robotics (RO5300)
  • Robotics (CS2500)

Follow this link to register for the course:

Location & Time: Room: Seminarraum Informatik 5 (Von Neumann) 2.132 12.15 – 14.00

Course materials and dates (tentative slides, last update Sep. 27, 2018)

  1. Probabilistic Learning for Robotics Intro (L1: October, 18th)
  2. Introductions to Topics I-III: Bayesian Inference, Gaussian Processes & Kalman/P. Filters (L2: October, 25th)
  3. Introductions to Topics IV-VI: Bayesian Optimization, Spiking Networks for Planning, Probabilistic Movement Primitives (L3: November, 1st)