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
- Kinematics, Dynamics & Simulation
- Representations of Skills & Imitation Learning
- Feedback Control, Priorities & Torque Control
- Planning & Cognitive Reasoning
- Reinforcement Learning & Policy Search
Ask questions at our course related Q&A page here.
Follow this link to register for the course: https://moodle.uni-luebeck.de
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) | Topics | Links | |
|---|---|---|---|---|
| 07.04 | VO | An Introduction to Humanoid Robotics | Slides | |
| 22.04 | UE | 0 Basics | Matrix, Vectors, Inv. kinematics, gradient desc. | |
| 28.04 | UE | 0 Basics | Mechanical & dynamical systems | |
| 29.04 | UE | 0 Basics | Mechanical & dynamical systems | |
| 05.05 | UE | 0 Basics | Differential equations & numerical solutions | |
| 06.05 | UE | 0 Basics | V-Rep simulation env. | |
| 12.05 | VO | I Kinematics, Dynamics & Simulation | Classical forward and inverse kinematics | |
| 13.05 | VO | I Kinematics, Dynamics & Simulation | Forward & inverse kinematics for control | |
| 19.05 | VO | I Kinematics, Dynamics & Simulation | Robot dynamics and numerical integration methods | |
| 20.05 | UE | Assignment I | Inverse Kinematics | |
| 26.05 | VO | II Representations of Skills & Imitation Learning | Dynamical systems movement primitives and outlook of probabilistic and neural primitives | |
| 27.05 | UE | 0 Basics | Statistics, Bayes, Gaussian distributions | |
| 02.06 | VO | III Feedback Control, Priorities & Torque Control | Classical PID Control & rigid body dynamics | |
| 03.06 | UE | Assignment II | Dynamical systems movement primitives | |
| 09.06 | VO | III Feedback Control, Priorities & Torque Control | Optimal Feedback Control with LQRs | |
| 10.06 | UE | I to II | Recap and Q&As | |
| 16.06 | VO | IV Reinforcement Learning | Optimal planning in Markov decision problems | |
| 17.06 | UE | Assignment III | PID and LQR control | |
| 23.06 | VO | IV Reinforcement Learning | Markov decision processes, value iteration, Q-learning, Deep Q-Learning | |
| 24.06 | VO | V Planning & Cognitive Reasoning | Sampling based planning, RRT | |
| 30.06 | VO | Outlook of Advanced Topics | ||
| 01.07 | UE | Assignment IV | Planning, RRT | |
| 08.07 | UE | Example Exam | ||
| 14.07 | VO | Summary & potential exam questions | ||
| 15.07 | UE | Assignment 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.