Karriere und Jobs

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.

Proposed date: 22/11/2024

Background:

Shared autonomy refers to a collaborative control paradigm wherein both a human operator and an autonomous robotic system share the responsibility of executing a task [1]. This approach leverages the strengths of human intelligence—such as intuition, adaptability, and decision-making—alongside the precision, repeatability, and computational power of robots. Consequently, this system is capable of managing complex tasks while enhancing efficiency, safety, and usability.

In this study, we focus on addressing the industrial assembly task through teleoperation skills. However, due to low transparency of the system and human-interaction manner, tackling contact-rich manipulation tasks, particularly those involving tight-clearance manipulation, remains a significant challenge. To remedy this gap, we propose integrating the knowledge gained from robotic assembly tasks into teleoperation within a shared-autonomy framework.  

Your Tasks:

1. Understand our previous solution(code) for solving the shared autonomy teleoperation work [2] and tight-clearance industrial insertion tasks with for force domain wiggle motion [3,4].
2. Propose the autonomy allocation method in our application.
3. Integrate the force domain wiggle motion into our teleoperation system based on the shared autonomy under our guidance.
4. Make experiments to demonstrate the feasibility and superiority of this method.

Requirement:

• Highly self-motivated;
• Experiences or knowledge from related Robotics courses;
• C++ and python programming experience.

To apply:

Send your personal CV and transcript as attachment to both yansong.wu(at)tum.de and xiaoyu.chen(at)tum.de.
 

Job Description:

t3://file?uid=4525