Thesis work - Learning Robot-Individual Physics-Models from Calibration Data and Test Measurements

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This role sits within ABB's Robotics business, a leading global robotics company. We're entering an exciting new chapter as we’ve announced the plan for SoftBank Group to acquire ABB Robotics. SoftBank is a globally recognized technology group and investor/operator focused on AI, robotics, and next-generation computing.  By joining us now, you’ll be part of a pioneering team shaping the future of robotics—working alongside world-class experts in a fast-moving, innovation-driven environment.

This Position reports to:

R&D Team Lead

 

Your role and responsibilities

Within ABB Robotics R&D Motion Control department we are proposing several Master thesis next spring. The Motion Control department are responsible for a wide range of areas within the robot controller development spanning from modeling, identification, control design and as well as optimization for path planning and numerous other motion control functionalities.

 

Background

Industrial robots of the same type exhibit significant robot-to-robot variations in positioning accuracy and dynamic performance. These variations originate from manufacturing tolerances, assembly differences, calibration differences, and other mechanical factors that are not fully captured by the nominal physics-based robot model. As part of the production process, robot-specific information is collected, for example data for robot calibration and quality verification. However, they may also contain valuable information about the individual robot's mechanical characteristics and dynamic behavior. The availability of fleet-level production data creates an opportunity to investigate whether machine-learning methods can extract robot-specific information and use it to improve physical robot models, enable individualized robot representations, detect anomalies, and support future digital twin applications.

 

Objective

The objective of this thesis is to investigate how data and test measurements collected during production process can be used to learn robot-specific characteristics and improve modeling of individual robots. The thesis will explore whether robot-specific information can be extracted from existing data and how such information can be integrated with physics-based models using machine-learning techniques. The long-term vision is to establish a framework where data collected during normal production procedures are continuously reused to create and maintain robot-individual models. Such models could support improved simulation accuracy, automated diagnostics, and future robot-specific control models without requiring dedicated identification experiments for every robot individual.

 

Details:

• Period: 2027 January – June
• Number of credits:  30
• Number of students for this thesis work: One ore two
• Location: ABB Robotics office at Finnslätten Västerås

Qualifications for the role

• Studying for a Master of Science, Engineering Physics, Electrical Engineering, Mechanical engineering or Computer Science degree.
• Strong interest in machine learning, system identification, physics-based modeling and control theory.
• Knowledge of Matlab and/or Python, numerical methods, and basic programming in C/C++ is considered an advantage
  

More about us

We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to read more about us and learn about the impact of our solutions across the globe.

 

Recruiting Manager "Johan Norén" , will answer your questions.

 

Positions are filled continuously. Please apply with your CV, academic transcripts, and a cover letter in English.

 

We look forward to receiving your application!

A Future Opportunity
Please note that this position is part of our talent pipeline and not an active job opening at this time. By applying, you express your interest in future career opportunities with ABB.

We value people from different backgrounds. Could this be your story? Apply today or visit www.abb.com to learn more about us and see the impact of our work across the globe.