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Multifingered Robot Hand Simulation and Control
Human-Robot Interaction
Robot Control
Telepresence
Human modeling
Brain Computer Interface Systems
Robot Learning
Apply before April 30, 2025
Background
Uncertainty poses a significant challenge in modern control systems, particularly for real-time networked applications. This research project explores cutting-edge online learning techniques using distributed Gaussian Processes (GPs) to enhance the performance of networked control systems under uncertainty [1,2]. You will design a computationally efficient, real-time learning framework to enable adaptive and robust control, focusing on Euler-Lagrange systems with unknown dynamics. The approach integrates a data-driven model, accounts for communication delays, and adapts to dynamic network conditions [3]. Stability will be rigorously analyzed using the Lyapunov theorem, with the proposed controller validated through simulations and experiments on FRANKA robots.
Your Tasks
- Develop an online GP-based learning framework for networked control systems
- Investigate real-time inference methods for GPs to ensure computational efficiency
- Tackle challenges such as communication delays and distributed learning
- Implement and evaluate the framework in simulation and on real robotic hardware
Requirements
- Highly self-motivated and self-independent
- Solid knowledge on Machine Learning, Control Theory and Robotics
- Python and C++ and/or MATLAB programming experience
Application Process
To apply, send your CV, transcript, and supporting documents to Dr. Zewen Yang (zewen.yang(at)tum.de) and Dr. Hamid Sadeghian (hamid.sadeghian(at)tum.de), before 30 April 2025.
Chair of Robotics and Systems Intelligence (RSI)
Munich Institute of Robotics and Machine Intelligence (MIRMI)
Technical University of Munich
Reference:
[1] Zewen Yang, Xiaobing Dai, Hirche Sandre. "Asynchronous Distributed Gaussian Process Regression." In the Thirty-Ninth Conference on Artificial Intelligence (AAAI), Philadelphia, Pennsylvania, USA, 2025.
[2] Zewen Yang, Songbo Dong, Armin Lederer, Xiaobing Dai, Siyu Chen, Stefan Sosnowski, Georges Hattab, and Sandra Hirche. "Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies." In 2024 American Control Conference (ACC), pp. 560-567. IEEE, 2024.
[3] Xiao Chen, Youssef Michel, Hamid Sadeghian, and Sami Haddadin. "Network-aware Shared Autonomy in Bilateral Teleoperation." In 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), pp. 888-894. IEEE, 2024.
Electronics
Mechatronics System Developement
Master Thesis (2 offers)
Project description: The Integrated Bi-Stiffness Actuator (iBSA) [1,2] consists of modules such as motor, spring, brake, clutch, and link (see Fig. 1). With these modules, the actuator can be configured in various operating modes. It also consists of various sensors which enable multiple sensor feedback. The objective is to develop a Modular Low-level Control Framework for Modular Actuator with Multiple Sensor Feedback. By exploiting the modular structure characteristic of the BSA, the effects of each element such as link decoupling, the performance of the elastic element, stiffness brake, and motor control can be tested independently. Moreover, other factors such as friction, hysteresis, effects of coupling and mechanical play between parts, and the effect of switching which creates instantaneous change in velocity can also be analyzed and their effect reduced through control at low-level. The end goal is to integrate the controllers into a generalized framework for the combined system.
Prerequisites:
- Master-level studies in Robotics, Electrical Engineering, Mechanics, Electronics, Computer Science
- Background in robotics with basic understanding of manipulator kinematics and dynamics.
- Dynamic systems modeling techniques
- Background in hardware development and system integration
- Good programming skills with Matlab, Python, and C++
- Working skills in the Ubuntu operating system
- Knowledge of parameter estimation algorithms and sensor fusion frameworks such as Kalman filter is a plus
- Experience in design of experiments is a plus
- E. P. Fortunić, M. C. Yildirim, D. Ossadnik, A. Swikir, S. Abdolshah and S. Haddadin, “Optimally Controlling the Timing of Energy Transfer in Elastic Joints: Experimental Validation of the Bi-Stiffness Actuation Concept,” in IEEE Robotics and Automation Letters, vol. 8, no. 12, pp. 8106-8113, Dec. 2023, doi: 10.1109/LRA.2023.3325782.
- D. Ossadnik et al., “BSA - Bi-Stiffness Actuation for optimally exploiting intrinsic compliance and inertial coupling effects in elastic joint robots,” 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 2022, pp. 3536-3543, doi: 10.1109/IROS47612.2022.9981928.
To apply, send your CV, cover letter, and supporting documents to Samuel Kangwagye (s.kangwagye(at)tum.de).
Chair of Robotics Science and Systems Intelligence
Munich Institute of Robotics and Machine Intelligence
Technical University of Munich
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