Advanced Robot Control and Learning
| Lecturer (assistant) | |
|---|---|
| Number | 0000004963 |
| Type | lecture |
| Duration | 2 SWS |
| Term | Wintersemester 2025/26 |
| Language of instruction | English |
| Position within curricula | See TUMonline |
- 13.10.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 20.10.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 27.10.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 03.11.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 10.11.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 17.11.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 24.11.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 01.12.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 08.12.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 15.12.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 22.12.2025 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 12.01.2026 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 19.01.2026 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 26.01.2026 10:00-12:00 Online: Videokonferenz / Zoom etc.
- 02.02.2026 10:00-12:00 Online: Videokonferenz / Zoom etc.
Admission information
Objectives
After successful participation in the module, the students are able to:
- to understand the theory behind different approaches in motion and interaction control of robot manipulators
- Solve modern control problems with advanced methods
- Understand and exploit the concept of redundancy in robotics
- Understand and use learning methods in robotics
- to understand the theory behind different approaches in motion and interaction control of robot manipulators
- Solve modern control problems with advanced methods
- Understand and exploit the concept of redundancy in robotics
- Understand and use learning methods in robotics
Description
This course delves into advanced methodologies in robot control, integrating theoretical foundations with modern approaches applicable to real-world robotic systems. The course begins with essential preliminaries in robotics and nonlinear control, establishing a strong base in kinematics, dynamics, and system stability. It then progresses through a spectrum of control techniques including model-based, adaptive, and robust control methods. A significant portion of the course is dedicated to modern challenges in controlling redundant robots, force and impedance control strategies, and advanced passivity-based control methods, especially in the context of teleoperation. Students will develop both theoretical insights and practical skills, reinforced through simulations and case studies of contemporary robotic systems.
1. Introduction
2. Preliminaries in Robotics:
Dynamics
Differential Kinematics
3. Preliminaries in nonlinear control
State Space Representation
Stability Analysis
4.Motion Control :
Joint Space and Task Space Control
Model-Based Control Approaches
Adaptive and Robust Control
Redundant Robot Control
5.Interaction Control
Force Impedance Control
Energy based approach/ Tank Energy
6. Networked robots
Bilateral teleoperation
Passivity based control
7. Robot Learning
1. Introduction
2. Preliminaries in Robotics:
Dynamics
Differential Kinematics
3. Preliminaries in nonlinear control
State Space Representation
Stability Analysis
4.Motion Control :
Joint Space and Task Space Control
Model-Based Control Approaches
Adaptive and Robust Control
Redundant Robot Control
5.Interaction Control
Force Impedance Control
Energy based approach/ Tank Energy
6. Networked robots
Bilateral teleoperation
Passivity based control
7. Robot Learning
Prerequisites
- Fundamentals of control engineering
- Fundamentals of robotics
- Fundamentals of robotics
Teaching and learning methods
Exercises are made available, presented and discussed during the lessons. Sample solutions are provided.
Through the homeworks and the provided project, students will gain hands-on experience in solving real-world problems using robotic systems.
- Exercises with solutions
- hands-on experience by the provided projects
- Tutorials
Through the homeworks and the provided project, students will gain hands-on experience in solving real-world problems using robotic systems.
- Exercises with solutions
- hands-on experience by the provided projects
- Tutorials
Examination
The evaluation are based on homeworks, final project and a final exam.
Recommended literature
Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Springer, 2025.
J.J. Slotine, W. Li, Applied Nonlinear Control, Prentice-Hall 1991
R. Kelly, V. Santibanez and A. Loria, Control of Robot Manipulators in Joint Space, 2005
B. Siciliano, O. Khatib, Springer Handbook of Robotics, 2008.
Ott, Christian. Cartesian impedance control of redundant and flexible-joint robots. Springer, 2008.
Hatanaka, Takeshi, et al. Passivity-Based Control and Estimation in Networked Robotics. Springer, 2015
J.J. Slotine, W. Li, Applied Nonlinear Control, Prentice-Hall 1991
R. Kelly, V. Santibanez and A. Loria, Control of Robot Manipulators in Joint Space, 2005
B. Siciliano, O. Khatib, Springer Handbook of Robotics, 2008.
Ott, Christian. Cartesian impedance control of redundant and flexible-joint robots. Springer, 2008.
Hatanaka, Takeshi, et al. Passivity-Based Control and Estimation in Networked Robotics. Springer, 2015