Humanoid Robotics (RO5300) SS2019

[Links to previous courses: SS2018]

During the summer semester Prof. Dr. Elmar Rueckert is teaching the course 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:

Location & Times

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 Feb. 15th, 2019)

Dates Chapter (0 to V)TopicsLinks
02.04VOAn Introduction to Humanoid RoboticsSlides
03.04UE0 Basics Matrix, Vectors, Inv. kinematics, gradient desc.Exercise, Solution, Files
09.04VOI Kinematics, Dynamics & SimulationClassical forward and inverse kinematicsSlides
10.04VOI Kinematics, Dynamics & SimulationForward & inverse kinematics for controlSlides
16.04UE0 Basics Mechanical & dynamical systemsExercise, Solution
17.04UE0 Basics Mechanical & dynamical systemsSee 16.04.
23.04UE0 Basics Differential equations & numerical solutionsExercise, Solution, Files
24.04UE0 Basics V-Rep simulation env. Exercise, Solution
30.04UEAssignment IInv. kinematicsExercise, Solution, Latex Draft, Files
07.05VOI Kinematics, Dynamics & SimulationRobot dynamics and numerical integration methodsSlides
08.05UE0 Basics Statistics, Bayes, Gaussian distributionsExercise, Solution
14.05VOII Representations of Skills & Imitation LearningDynamical systems movement primitives and outlook of probabilistic and neural primitivesSlides
15.05UEAssignment IIDynamical systems movement primitivesExercise, Solution, Files
21.05VOIII Feedback Control, Priorities & Torque ControlClassical PID Control & rigid body dynamicsSlides
22.05VOIII Feedback Control, Priorities & Torque ControlOptimal Feedback Control with LQRsSlides
28.05UEI to IIRecap and Q&As
29.05UEAssignment IIIPID and LQR controlExercise, Solution
04.06VOIV Reinforcement Learning Optimal planning in Markov decision problemsSlides
05.06VOIV Reinforcement LearningMarkov decision processes, value iteration, Q-learning, Deep Q-LearningSlides
11.06VOV Planning & Cognitive ReasoningSampling based planning, RRT Slides
18.06VO Outlook of Advanced TopicsCancelled
19.06UEAssignment IVPlanning, RRTExercise, Solution, Files
25.06UEExample ExamExam, Exam Solution
09.07VOSummary & potential exam questionsSlides
10.07UEAssignment IV Presentations
15.07Date of the written exam
Geb. 64 Erdg. Raum S2/S3, 14:00-15:45
07.10Date of the written exam. Geb. 64 Erdg. Seminarraum Minsky, 10:00-11:45

Bonus Points and Group Numbers

You can find the list of bonus points as well as the list of group numbers regarding the assignments here.

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.