Master Thesis in Reinforcement Learning for Multiple-Embodiment Grasping

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 GmbHis looking forward to your application!

Job Description

• During your Masterthesis, you will design architectures and pipelines to transfer current methods to an online RL algorithm.
• You will create RL environments for multi-embodiment grasping using our grasp dataset generation pipeline.
• Furthermore, you will benchmark the current state of the art approaches in robotics grasping.
• Finally, you will work under the remote and on-site supervision of the research staff, who will guide you throughout your thesis. Most of the systems and frameworks are already in place, giving you an excellent opportunity to hone your skills.

Qualifications

• Education: Master studies in the field of Computer Science, Machine Learning, Artificial Intelligence or comparable
• Experience and Knowledge: proficient in Python (including best practices in code structure and packaging); Machine Learning (such as PyTorch, JAX and TensorFlow); physics simulators (such as MuJoCo, Bullet or Isaac Sim); familiarity with high-level graphics libraries such as Open3D is a plus
• Personality and Working Practice: a goal-oriented person with a structured and analytical mindset
• Languages: fluent in written and spoken German or 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?
Roman Freiberg (Functional Department)
+49 711 811 23559

#LI-DNI

Summary

• Type: Full-time
• Function: Research