Dr. Sabrina Hoppe
Algorithm Toolbox Development for AI in Production Systems
"AI algorithms for a variety of real-world production systems"
After studying and gaining some research experience in computer science and cognitive science, I started at Bosch in 2016 as a PhD student. In my dissertation, I investigated methods to improve sample efficiency, i.e. how to speed up learning, for self-learning robots in contact-rich manipulation tasks in production settings.
In 2019 I took a position as research engineer with Bosch Research, so ever since I get to investigate pragmatic ways how to apply and enhance state-of-the-art machine learning methods on data from and for Bosch production - for self-learning robots and beyond.
Since 2022, I lead an activity for AI toolbox development in production systems and push our algorithms to a wide range of applications.
Curriculum vitae
- PhD student
- Research engineer
- Activity Lead
Selected publications
Hoppe et al. (2020)
- International Conference on Intelligent Robots and Systems (IROS)
Hoppe et al. (2020)
- Hoppe S., Toussaint M. / arxiv preprint
Hoppe et al. (2019)
- IEEE Robotics and Automation Letters
Hoppe et al. (2018)
- Hoppe, S., Loetscher, T., Morey, S., & Bulling, A.
- Frontiers in Human Neuroscience
Interview with Dr. Sabrina Hoppe
Activity Leader for AI Toolbox Development
Please tell us what fascinates you most about research.
A variety of open questions to think about, sharpening my understanding of technology while pushing it to the future, exchanging ideas with others, being able to go off the beaten path.
What makes research done at Bosch so special?
At Bosch I am in close dialog with colleagues from various backgrounds as well as practitioners in the production plants and get first-hand feedback about the potential impact or feasibility of my work for their day to day business and challenges.
What research topics are you currently working on at Bosch?
I am conducting research on various methods for smart production systems: from reinforcement learning for robots, to self-supervised learning on an abundance of existing data, to iteractive data analyses for domain experts in Bosch production plants.
What are the biggest scientific challenges in your field of research?
In machine learning, almost everything is possible given enough data, often also including expert annotations. In practice, this is not readily available and very costly to acquire though. So the challenge in my work is to identify applications where learning is feasible, then design methods to collect the right kind of data and employ sample-efficient learning algorithms. I therefore invest a lot of my time in systematically evaluating ideas and prototypes.
How do the results of your research become part of solutions "Invented for life"?
Every improvement in the Bosch production systems directly contributes to the quality, cost and reliability of Bosch products that can be found in almost all areas of life: at home, in e-bikes, smartphones and many more.
Get in touch with me
Dr. Sabrina Hoppe
Research engineer in production automation