Master Thesis in Reinforcement Learning for Multi-Embodiment Grasping

Vor 6 Tagen


Renningen, Baden-Württemberg, Deutschland Bosch Vollzeit
Job Opportunity

We are seeking a highly motivated and skilled Master student to work on a research project focused on Reinforcement Learning for Multi-Embodiment Grasping.

Project Overview

During this Master thesis, you will design and develop 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. Additionally, you will benchmark the current state of the art approaches in robotics grasping.

Responsibilities
  • Design and develop architectures and pipelines for online RL algorithms
  • Create RL environments for multi-embodiment grasping
  • Benchmark current state of the art approaches in robotics grasping
  • Work under the supervision of research staff to guide your thesis
Requirements
  • Master studies in Computer Science, Machine Learning, Artificial Intelligence, or comparable
  • Proficient in Python, including best practices in code structure and packaging
  • Machine Learning skills, including PyTorch, JAX, and TensorFlow
  • Familiarity with physics simulators, such as MuJoCo, Bullet, or Isaac Sim
  • Goal-oriented person with a structured and analytical mindset
  • Fluent in written and spoken German or English
What We Offer

We offer a dynamic and inclusive work environment, with opportunities for professional growth and development. Our team is committed to diversity and inclusion, and we welcome applications from candidates with diverse backgrounds and experiences.

Please attach your CV, transcript of records, examination regulations, and if indicated, a valid work and residence permit.



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