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

- 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

**Location & Times**

- VO 10:15-11:45 Seminarraum Mathematik 1 ( Banach ), building 64, 3rd floor
- UE 10:15-11:45 Seminarraum Informatik 4 ( Minsky ), building 64, basement.

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 sp****ec****ial 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) | Topics | Links | |
---|---|---|---|---|

02.04 | VO | An Introduction to Humanoid Robotics | Slides | |

03.04 | UE | 0 Basics | Matrix, Vectors, Inv. kinematics, gradient desc. | Exercise, Solution, Files |

09.04 | VO | I Kinematics, Dynamics & Simulation | Classical forward and inverse kinematics | Slides |

10.04 | VO | I Kinematics, Dynamics & Simulation | Forward & inverse kinematics for control | Slides |

16.04 | UE | 0 Basics | Mechanical & dynamical systems | Exercise, Solution |

17.04 | UE | 0 Basics | Mechanical & dynamical systems | See 16.04. |

23.04 | UE | 0 Basics | Differential equations & numerical solutions | Exercise, Solution, Files |

24.04 | UE | 0 Basics | V-Rep simulation env. | Exercise, Solution |

30.04 | UE | Assignment I | Inv. kinematics | Exercise, Solution, Latex Draft, Files |

07.05 | VO | I Kinematics, Dynamics & Simulation | Robot dynamics and numerical integration methods | Slides |

08.05 | UE | 0 Basics | Statistics, Bayes, Gaussian distributions | Exercise, Solution |

14.05 | VO | II Representations of Skills & Imitation Learning | Dynamical systems movement primitives and outlook of probabilistic and neural primitives | Slides |

15.05 | UE | Assignment II | Dynamical systems movement primitives | Exercise, Solution, Files |

21.05 | VO | III Feedback Control, Priorities & Torque Control | Classical PID Control & rigid body dynamics | Slides |

22.05 | VO | III Feedback Control, Priorities & Torque Control | Optimal Feedback Control with LQRs | Slides |

28.05 | UE | I to II | Recap and Q&As | |

29.05 | UE | Assignment III | PID and LQR control | Exercise, Solution |

04.06 | VO | IV Reinforcement Learning | Optimal planning in Markov decision problems | Slides |

05.06 | VO | IV Reinforcement Learning | Markov decision processes, value iteration, Q-learning, Deep Q-Learning | Slides |

11.06 | VO | V Planning & Cognitive Reasoning | Sampling based planning, RRT | Slides |

18.06 | VO | Outlook of Advanced Topics | Cancelled | |

19.06 | UE | Assignment IV | Planning, RRT | Exercise, Solution, Files |

25.06 | UE | Example Exam | Exam, Exam Solution | |

09.07 | VO | Summary & potential exam questions | Slides | |

10.07 | UE | Assignment IV Presentations | ||

15.07 | Date of the written exam Geb. 64 Erdg. Raum S2/S3 | |||

07.10 | Tentative date of the written exam | |||

### 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.