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Sabrina Hoppe

Self-learning robots and data analysis for production automation

"It always seems impossible until it's done." Nelson mandela
Sabrina Hoppe

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 in the same field, 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.

Curriculum vitae

  1. PhD student
  2. Research engineer

Selected publications

  • Publications

    Hoppe et al. (2020)

    Sample-Efficient Learning for Industrial Assembly using Qgraph-bounded DDPG
    • International Conference on Intelligent Robots and Systems (IROS)
  • Publications

    Hoppe et al. (2020)

    Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning
    • Hoppe S., Toussaint M. / arxiv preprint
  • Publications

    Hoppe et al. (2019)

    Planning Approximate Exploration Trajectories for Model-Free Reinforcement Learning in Contact-Rich Manipulation
    • IEEE Robotics and Automation Letters
  • Publications

    Hoppe et al. (2018)

    Eye Movements During Everyday Behavior Predict Personality Traits
    • Hoppe, S., Loetscher, T., Morey, S., & Bulling, A.
    • Frontiers in Human Neuroscience

Interview with Sabrina Hoppe

Sabrina Hoppe

Research engineer in production automation

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

Sabrina Hoppe
Research engineer in production automation