The exercise will enable the application of modern machine learning techniques and tools in robotics / cyber-physical systems. The following topics will be covered in the course:
– Kinematics, dynamics & simulation of CPS.
– Data representations & model learning.
– Control techniques, priorities & torque control.
– Planning & cognitive reasoning.
– Reinforcement learning and black-box optimization.
– Kinematics, dynamics & simulation of CPS.
– Data representations & model learning.
– Control techniques, priorities & torque control.
– Planning & cognitive reasoning.
– Reinforcement learning and black-box optimization.
The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems.
Links and Resources
- MUOnline
- Latex Template for the Assignments
- CodeWithMe
Online Streaming:
- Live Streaming of the lectures via WEBEX
Slides & Code Examples:
- Org & Intro (L1) 06.10.2021
- Slides on Python (L2) 13.10.20212
- Python Code Basics (L2) 13.10.2021 [Python Code]
- Kinematics, Dynamics & Simulation (L3) 20.10.2021 (CodeWithMe Link)[Python Code]
- Data Representations & Learning (L4) 27.10.2021 (Instructions of Howto use CoppeliaSim with Python)
- Feedback Control (L5) 03.11.2021
- Planning (L6) 17.11.2021
- Reinforcement Learning (L7) 24.11.2021
Assignments: - Assignment I (due to 23.12.2021 13:00 CET)
- Assignment II (due to 04.02.2022 23:59 CET)
Location & Time
- Location: HS Phys.Chemie (03CH01171)
- Dates: Wednesdays 10:15 – 12:00 Winter semester
Learning objectives / qualifications
- Students get a comprehensive understanding of Cyber-Physical-Systems.
- Students learn to analyze the challenges in simulating, modeling and controlling CPS.
- Students understand and can apply basic machine learning and control techniques in CPS.
- Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.
Programming Assignments & Simulation Tools
For simulating robotic systems, we will use the tool CoppeliaSim. The tool can be used for free for research and for teaching.
To experiment with state of the art robot control and learning methods Python will be used. If you never used Python and are unexperienced in programming, please visit the tutorials on Python programming prior to the lecture.
The course will also use the tool Code With Me from JetBrains. With this stool, we can develop jointly code.
Literature
- The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert.
- Bishop 2006. Pattern Recognition and Machine Learning, Springer.
- Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press.
- Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press.
- B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
- Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.