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Master Thesis in Reinforcement Learning for Multiple-Embodiment Grasping 15.09.2024 Bosch-Gruppe Renningen
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Master Thesis in Reinforcement Learning for Multiple-Embodiment Grasping
Renningen
Aktualität: 15.09.2024

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15.09.2024, Bosch-Gruppe
Renningen
Master Thesis in Reinforcement Learning for Multiple-Embodiment Grasping
Aufgaben:
During your Master thesis, 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.
Qualifikationen:
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 Languages: fluent in written and spoken German or English

Berufsfeld

Bundesland

Standorte