Humanoid Robotics (RO5300)[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
- Kinematics, Dynamics & Locomotion
- Representations of Skills & Imitation Learning
- Feedback Control, Priorities & Torque Control
- Reinforcement Learning & Policy Search
- Sensor Integration & Fusion
- Cognitive Reasoning & Planning
The exam results are out. Please visit our moodle page to find your grade. The second exam date is on Sept. 27th, 2018 10 am – 12 am in our seminar room Nr.64 in building 64.
Ask questions at our course related Q&A page here.
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.
Follow this link to register for the course: https://moodle.uni-luebeck.de/course/view.php?id=3261.
Location: Room: C4-S03
Course materials (tentative slides, last update Feb. 27, 2018)
- Humanoid Robotics Intro (L1: April, 10th)
- Kinematics, Dynamics & Locomotion (L2: April, 17th, L3: April, 24th)
- Representations of Skills & Imitation Learning (L4: May 8th, L5: May 15th)
- Feedback Control, Priorities & Torque Control (L6: May 30th, L7: June 5th)
- Reinforcement Learning & Policy Search(L8: June 19th, L9: June 26th, L10: July 3rd)
- Sensor Integration & Fusion and Cognitive Reasoning & Planning (L11: July 10th)
- Exam (L12: July 17th, 2018, Handout PDF)
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.
- Introduction to Matlab and V-Rep (Exercise, Solution)
Tutorial: April, 18th 2018 in room C4-S03.
- Kinematics and Dynamics Assignment (Exercise/Assignment, Solution, Material, Latex Draft)
Tutorial: May, 2nd 2018, Submission: May 22nd, 2018 at 10 am.
- Movement Representations (Exercise/Assignment, Solution, Material)
Tutorial: May, 16th 2018, Submission: June 08th, 2018 at 10 am.
Videos to the exercise
- Feedback Control (Exercise/Assignment, Solution)
Tutorial: May, 30th 2018, Submission: June 22nd, 2018 at 10 am.
- Path Planning and Reinforcement Learning (Exercise/Assignment, Solution, Material)
Tutorial: June, 20th 2018, Submission: July 10th, 2018 at 10 am
Matlab Files shown during the Tutorial can be found here.
Probabilistic Learning for Robotics (RO5601)[WS2018/19] In the winter semester, I will teach a course on Probabilistic Learning for Robotics which covers advanced topics including graphical models, factor graphs, probabilistic inference for decision making and planning, and computational models for inference in neuroscience. The content is not yet fixed and may change.
In accompanying exercises and hands on tutorials the students will experiment with state of the art machine learning methods and robotic simulation tools. In particular, Mathworks’ MATLAB, the robot middleware ROS and the simulation tool V-Rep will be used.