Humanoid Robotics (RO5300) SS2020

[Links to previous courses temporary deactivated: SS2018, SS2019]

Important Notice: This course is organized through online lectures and exercises. Details to the organizations will be discussed in our

FIRST MEETING: 07.04.2020 10:15-11:45 WEBEX Slides

using the webex tool. Please follow the instructions of the ITSC here to setup your computer. Click on the links to create a google calendar event, joint the webex meeting or to access the online slides.

during a webex meeting. The slides are available here. Click on the links to create a google calendar event, joint the webex meeting or to access the online slides.

Dates & Times of the Online Webex Meetings

  • Lectures are organized on TUESDAYS, 10:15-11:45, Webex Link
  • Exercises are organized on WEDNESDAYS, 10:15-11:45, Webex Link

Course Description

Prof. Dr. Elmar Rueckert is teaching the course on Humanoid Robotics (RO5300) together with M.Sc. Nils Rottmann, who supervises the exercises. In this course he discusses the key components of one of the most complex autonomous systems. These topics are

  1. Kinematics, Dynamics & Simulation
  2. Representations of Skills & Imitation Learning
  3. Feedback Control, Priorities & Torque Control
  4. Planning & Cognitive Reasoning
  5. Reinforcement Learning & Policy Search

Ask questions at our course related Q&A page here.

Follow this link to register for the course:

This course provides a unique overview over central topics in robotics. A particular focus is put in the dependencies and interaction among the components in the control loop illustrated in the image above. These interactions are discussed in the context of state of the art methods including dynamical systems movement primitives, gradient based policy search methods or probabilisitic inference for planning algorithms.

In sum,  the lecture provides a structured and well motivated overview over modern techniques and tools which enable the students to define reward functions, implement robot controller and interaction software and to apply and extend state of the art reinforcement learning and planning approaches.

No special knowledge is required beforehand. All concepts and theories will be developed during the lectures or the tutorials.

The students will also experiment with state of the art machine learning methods and robotic simulation tools in accompanying exercises. Hands on tutorials on programming with Matlab and the simulation tool V-Rep complement the course content.

Course dates & materials (tentative slides, last update April 1st, 2020)

Dates ChapterTopicsLinks to Slides, Vodcasts, Code, etc.
07.04VOAn Introduction to Humanoid RoboticsSlides, VodCast
08.04UE0 Basics Matrix, Vectors, Inv. kinematics, gradient desc.Exercise Sheet, Solution Sheet, Video
14.04VOI Kinematics, Dynamics & SimulationClassical forward and inverse kinematicsSlides,Vodcast (Sorry, at 4min13 the microphone was activated.)
15.04UE0 Basics Mechanical & dynamical systems (4 BP Sheet)Exercise Sheet, Video
21.04VOI Kinematics, Dynamics & SimulationForward & inverse kinematics for controlSlides, Vodcast
22.04UE0 Basics Mechanical & dynamical systems Solution Sheet, Video
28.04VOII Representations of Skills & Imitation LearningDynamical systems movement primitivesSlides, VodCast
29.04UE0 Basics Differential equations & numerical solutionsExercise Sheet, Solution Sheet, Video
05.05VOII Representations of Skills & Imitation LearningMuscle Synergies and Probabilistic Movement PrimitivesSlides, VodCast
06.05UE0 Basics V-Rep simulation env. (For the BPs send sreenshots combined in a PDF) Exercise Sheet, Solution Sheet, Video, AddOn
12.05VOIII Feedback Control, Priorities & Torque ControlClassical PID Control & rigid body dynamicsSlides, VodCast
13.05UEAssignment IInverse KinematicsExercise Sheet, Vrep, Solution Sheet, Video
19.05VOno lecture
20.05UE0 BasicsStatistics, Bayes, Gaussian distributionsExercise Sheet, Solution Sheet, Video
26.05UEAssignment IIDynamical systems movement primitivesExercise Sheet, Solution Sheet, Data, Video
27.05VOno lecture
02.06VOIV Reinforcement LearningMarkov decision processes, value iteration, Q-learning, Deep Q-LearningSlides, VodCast
03.06UEI to IIRecap and Q&As
09.06VOV Planning & Cognitive ReasoningSampling based planning, RRT Slides,
10.06UEAssignment IIIPID and LQR controlExercise Sheet, Solution Sheet, Video
16.06VOSummary & Outlook of Advanced TopicsBonus Point Exam (max. 10Pts)Slides, VodCast
24.06UEAssignment IVPlanning, RRTExercise Sheet, Solution Sheet, Video
08.07UEAssignment IV Presentations


The course grades will be computed solely from submitted student reports of four assignments, for each you can get 25 points. Two weeks after the assignment presentation events, the reports and the code have to be submitted (one report per team) to Below is the list of dates and deadlines. Please use Latex for submitting the assignments. A latex template can be found under

Presentation DateTopicsPointsSubmission Deadline
13.05.2020 10:15-11:45Assignment I Presentation2527.05.2020 10:00
27.05.2020 10:15-11:45Assignment II Presentation2510.06.2020 10:00
10.06.2020 10:15-11:45Assignment III Presentation2524.06.2020 10:00
24.06.2020 10:15-11:45Assignment IV Presentation2508.07.2020 10:00

Bonus Points

You can receive up to 30 Bonus Points (BP) during the course, 10 BP during the lectures and 20 BP for submitting optional exercise solutions. To get BPs during the lecture, you have to successfully participate at the quizz sessions at the beginning of each lecture. To get BPs for the optional exercise solutions, you have to (clearly and readable) write down your solution, take a photo and send it to with the concern Exercise_##_LastName, where ## is the number of the exercise. You have to send your solution prior to the start of the exercise session where we will cover the exercise sheet.

Points to Grades

>= PointsGradeComment
951.0Best possible grade
05.0Worst possible grade

Materials for the exercise

For simulating robot manipulation tasks we will use the simulator V-REP. For research and for teaching a free eduction version can be found here. To experiment with state of the art robot control and learning methods Mathworks’ MATLAB will be used. If you do not have it installed yet, please follow the instructions of our IT-Service Center.

Matlab Files shown during the Tutorial can be found here.