Control Engineer

Responsibilities & Tasks

Develop and implement advanced blind locomotion algorithms that enable the robot to navigate and manipulate objects without visual input.
Integrate actuators, sensors, and force feedback mechanisms to enhance stability and control.
Integrate sensor data from a variety of sources (e.g., IMUs, ground contact sensors) to create robust sensor fusion algorithms for state estimation, reliable locomotion, navigation and manipulation.
Design manipulation control frameworks that allow the humanoid robot to interact with its environment and manipulate objects in its environment.
Collaborate with the Simulation, Perception & Navigation, and Real-Time Systems Teams to ensure sensor integration and smooth locomotion in various environments.
Test, debug, and refine algorithms through real-world applications and iterative development.
Document algorithm development, testing results, and performance improvements for knowledge sharing.
General Responsibilities: Attend weekly/bi-weekly departmental & organizational RoboTUM meetings, as well as occasional spontaneous meetings as needed. Answer emails and messages within 24 hours (be comfortable using Slack). Adhere to RoboTUM policies and be willing to help when the team needs you

Position Benefits

Contribute to the development of the humanoid robot’s blind locomotion and manipulation algorithms, the main aspect of the humanoid and crucial for interaction and manipulation.
Gain hands-on experience in simulation, advanced control engineering, sensor fusion and robotic motion planning.
Work on a high-performance humanoid inspired by biomechanics.
Collaborate with power electronics, body mechatronics, arm mechatronics and software teams to develop a fully integrated robotic system.
Access to simulation software such as Isaac Sim, training environments such as Isaac Lab and prototyping facilities at TUM.
Expand your professional network through research collaborations, competitions, and industry connections.
Potential for thesis projects or specialized research into robotic simulation, and robot training with and without synthetic data.
Close-knit community of dedicated people.

Minimum Qualifications

Background in control engineering, robotic algorithms, or robot motion planning.
Experience with a simulation tool (e.g., Gazebo, MuJoCo, Isaac Sim, MATLAB/Simulink), and Python/ C++.
Understanding of sensor fusion, machine learning algorithms, and real-time control systems.
Ability to collaborate across interdisciplinary teams, combining software with hardware and sensor systems, and iterate based on real-world testing.
Strong problem-solving skills, analytical thinking, attention to detail, and enthusiasm for tackling complex challenges in legged locomotion.

Optimal Qualifications

Background in control engineering in legged robotic systems.
Experience in developing (blind) locomotion algorithms for autonomous systems or legged robots, and work on humanoid robots with physical interaction and manipulation.
High proficiency in Isaac Sim and Lab, or MATLAB Simulink, and one of the following: C++, Python. Experience with ROS 2.
Familiarity with reinforcement learning techniques for robot locomotion in various environments.
Good understanding of sensor fusion, machine learning algorithms, and real-time control systems.
Strong problem-solving skills, analytical thinking, attention to detail, and enthusiasm for tackling complex challenges in legged locomotion.
Project experience in interdisciplinary teams combining hardware and software.

Estimated Time Commitment

PDF Offer