Master Thesis Approximating Model Predictive Controllers Using Imitation Learning

Bosch Group; Renningen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

Bosch Group is offering a Master's thesis opportunity focusing on Approximate Model Predictive Control. Applicants will work on enhancing imitation learning procedures crucial for safety-critical applications, particularly in the automated driving domain. Candidates should have a background in Cybernetics, Engineering, or Computer Science, alongside a robust understanding of Machine Learning.

Qualifikationen

• Enrollment at university is required for this thesis.
• Profound knowledge in Machine Learning and control engineering.
• Experience with Python and relevant deep learning frameworks.

Aufgaben

• Extend statistical properties of IL procedure with convergence analysis.
• Investigate error estimations and stopping criteria.
• Deploy AMPC IL procedure to real-world automated driving problems.

Kenntnisse

Machine Learning

Control Engineering

Python

Analytical Thinking

Deep Learning frameworks (PyTorch, TensorFlow, Jax)

Systematic Working

Ausbildung

Master studies in Cybernetics, Engineering, Mathematics, or Computer Science

Jobbeschreibung

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

Approximate model predictive control (AMPC) has emerged as an approach to tackle the computational burden of MPC, aiming to approximate the MPC policy with a computationally cheaper surrogate, such as neural networks. So far, the standard approach to obtaining such a surrogate policy has been based on naive behavioral cloning. This approach, however, has significant drawbacks, resulting in the surrogate policy potentially failing to provide the original MPC guarantees. To tackle this, a tailored AMPC imitation learning (IL) procedure was developed recently, enabling consistent learning of a surrogate policy and ensuring that the learned policy maintains the original MPC safety and stability guarantees. This development allows for MPC-based control functions in safety-critical industrial settings.

• The goal of your thesis is to extend the statistical properties of the proposed IL procedure by analyzing the rate at which the learned policy converges to the MPC policy, ultimately aiming to provide sample bounds on the error between the policies.
• Moreover, the thesis could cover the investigation of more general error estimations, stopping criteria, and studies on sample efficiency.
• Last but not least, the thesis will also focus on the deployment of the developed AMPC IL procedure to a real-world automated driving problem, including a comparison with other existing approaches.

Qualifications

• Education: Master studies in the field of Cybernetics, Engineering, Mathematics, Computer Science or comparable
• Experience and Knowledge: profound knowledge of Machine Learning and control engineering; experience in Python, DL frameworks like PyTorch, TensorFlow or Jax
• Personality and Working Practice: you are an autonomous, systematic working person with analytical thinking
• Languages: very good in English

Additional Information

Start:according to prior agreement
Duration:6 months

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

Need further information about the job?
Felix Berkel (Functional Department)
[phone]
Elias Milios (Functional Department)
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