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 & Locomotion
  2. Representations of Skills & Imitation Learning
  3. Feedback Control, Priorities & Torque Control
  4. Reinforcement Learning & Policy Search
  5. Sensor Integration & Fusion
  6. Cognitive Reasoning & Planning

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

Introduction slides to the course on Humanoid Robotics by Prof. Dr. Elmar Rueckert, University of LuebeckThis 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.

Follow this link to register for the course:

Location: Seminar Room Math, building 64, 3rd floor.

Course materials (tentative slides, last update Jan. 3rd, 2019)

  1. Humanoid Robotics Intro (L1: April, 2nd)
  2. Kinematics, Dynamics & Locomotion (L2: April, 9th, L3: April, 16th)
  3. Representations of Skills & Imitation Learning (L4: May 7th, L5: May 14th)
  4. Feedback Control, Priorities & Torque Control (L6: May 21th, L7: May 28th)
  5. Reinforcement Learning & Policy Search(L8: June 4th, L9: June 11th, L10: June 18th)
  6. Sensor Integration & Fusion and Cognitive Reasoning & Planning (L11: June 28th(attention this is a Friday!), L12: July 2nd)
  7. Exam Q&A (L13: July 9th, Handout PDF)
  8. Tentative Exam Dates (L14: July 16th, L15: July 23rd)

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.

  1. Introduction to Matlab and V-Rep (Exercise, Solution)
    Tutorial: April, 17th in room Geb. 64, Seminarraum Dijkstra.
  2. Kinematics and Dynamics Assignment (Exercise/Assignment, Solution, Material, Latex Draft)
    Tutorial: May, 8th, Submission: TBA at 10 am. 
  3. Movement Representations (Exercise/Assignment, Solution, Material)
    Tutorial: May, 29th, Submission: TBA at 10 am.
    Videos to the exercise
  4. Path Planning and Reinforcement Learning (Exercise/Assignment, Solution, Material)
    Tutorial: June, 19th 2018, Submission: TBA at 10 am 

Matlab Files shown during the Tutorial can be found here.