Research internship

Location: LAAS-CNRS, Toulouse, FR

Starting Date: September-December 2025

Duration: 6 months

Salary: about 600 EUR net per month

Summary

The Robotics and Interactions Group at LAAS-CNRS is offering a research internship to contribute to the development of state-of-the-art aerial robotics. We are looking for a motivated intern to help extend recent advances in robust motion planning and control for autonomous quadrotors operating in uncertain and dynamic environments.

Full internship description: https://partage.laas.fr/index.php/s/nDbGLjKN2MTiJqS

About the Project:

In real-world applications such as search-and-rescue and package delivery, robots must operate reliably despite uncertainty in state estimation, model parameters, system dynamics, and obstacle localization. Achieving robust performance in these settings requires motion plans and control strategies that remain effective under such uncertainty.

Our approach leverages sensitivity analysis [1], a mathematical tool that quantifies how changes in initial system variables affect the robot’s future trajectory. Using this framework, our prior work has demonstrated robust control strategies in challenging scenarios such as navigating narrow windows, ring-catching, and avoiding moving obstacles. These tasks reflect real-world challenges like dexterous aerial manipulation and navigation in dense, multi-agent environments.

The goal of this project is to further advance these capabilities, enabling aerial robots to operate safely in dynamic, human-centered environments. The selected intern will explore how planning, control, and machine learning techniques can be combined to enhance robustness to external disturbances (e.g., wind) and environmental uncertainty (e.g., unpredictable human motion).

Responsibilities:

• Design novel methods for robust planning and control of aerial robots
• Develop and implement algorithms in simulation and on hardware
• Run experiments on quadrotors and analyze results
• Contribute to publications or technical documentation

Requirements:

• Pursuing a master’s degree in Robotics, EE, ME, CS, or a related field
• Strong foundation in controls, optimization, and/or probabilistic robotics
• Familiarity with Python, C++, and/or MATLAB
• Experience with ROS is a plus

What You'll Gain:

• Hands-on experience in advanced control algorithms for real-world robotics
• Exposure to cutting-edge research in robust planning and control
• Opportunity to co-author publications and contribute to open-source tools
• Mentorship from a dynamic research team working at the intersection of theory and practice

How To Apply:

Interested candidates are requested to submit their CV and a motivation letter to with the subject: [Internship LAAS - Control]. The selection procedures will consist of an interview that will be organized by Dr. James Zhu. The interview will be conducted in English. The candidate will be asked to present his/her previous experience and to discuss the project. The position will remain open until satisfactory candidates are found.

Supevisors' profiles:

• James Zhu: https://scholar.google.com/citations?user=lltvRaIAAAAJ
• Marco Cognetti - https://scholar.google.com/citations?user=NAq-dYcAAAAJ&hl
• Thierry Simeon - https://scholar.google.com/citations?user=L74V10IAAAAJ

For any questions, contact

References:

[1] P. Robuffo Giordano, Q. Delamare, and A. Franchi, “Trajectory generation for minimum closed-loop state sensitivity,” IEEE Int. Conf. on Robotics and Automation, pp. 286–293, 2018.

[2] S. Wasiela, M. Cognetti, P. Robuffo Giordano, J. Cortés, and T. Siméon, “Robust motion planning with accuracy optimization based on learned sensitivity metrics,” IEEE Robotics and Automation Letters, vol. 9, no. 11, pp. 10 113–10 120, 2024.

[3] J. Zhu, T. Siméon, and M. Cognetti, “Robust sensitivity-aware chance-constrained mpc for handling multiple sources of uncertainty,” (submitted to) IEEE Robotics and Automation Letters, 2025