Humanoid Robotics (RO5300) SS2020

[Links to previous courses: 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, 12:15-13:45
  • Exercises are organized on WEDNESDAYS, 10:15-11:45

Course Description

Prof. Dr. Elmar Rueckert is teaching the course on Humanoid Robotics (RO5300). 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 Chapter (0 to V)TopicsLinks
07.04VOAn Introduction to Humanoid RoboticsSlides
22.04UE0 Basics Matrix, Vectors, Inv. kinematics, gradient desc.
28.04UE0 Basics Mechanical & dynamical systems
29.04UE0 Basics Mechanical & dynamical systems
05.05UE0 Basics Differential equations & numerical solutions
06.05UE0 Basics V-Rep simulation env.
12.05VOI Kinematics, Dynamics & SimulationClassical forward and inverse kinematics
13.05VOI Kinematics, Dynamics & SimulationForward & inverse kinematics for control
19.05VOI Kinematics, Dynamics & SimulationRobot dynamics and numerical integration methods
20.05UEAssignment IInverse Kinematics
26.05VOII Representations of Skills & Imitation LearningDynamical systems movement primitives and outlook of probabilistic and neural primitives
27.05UE0 BasicsStatistics, Bayes, Gaussian distributions
02.06VOIII Feedback Control, Priorities & Torque ControlClassical PID Control & rigid body dynamics
03.06UEAssignment IIDynamical systems movement primitives
09.06VOIII Feedback Control, Priorities & Torque ControlOptimal Feedback Control with LQRs
10.06UEI to IIRecap and Q&As
16.06VOIV Reinforcement Learning Optimal planning in Markov decision problems
17.06UEAssignment IIIPID and LQR control
23.06VOIV Reinforcement LearningMarkov decision processes, value iteration, Q-learning, Deep Q-Learning
24.06VOV Planning & Cognitive ReasoningSampling based planning, RRT
30.06VO Outlook of Advanced Topics
01.07UEAssignment IVPlanning, RRT
08.07UEExample Exam
14.07VOSummary & potential exam questions
15.07UEAssignment IV Presentations
Date of the written exam
Date of the written exam.

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.