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Simulation environments offer a promising approach to address this challenge. By accurately modeling the underlying physics of assembly operations, it becomes possible to learn generalized representations of these processes. NVIDIA Isaac Sim and other simulation engines like MuJoCo enables high-fidelity physics simulation, sensor emulation for accurately simulating these assembly processes. Reinforcement learning (RL) and modern foundational models can then be leveraged to learn policies and predictive models that capture the dynamics of contact-rich interactions.
Setup and configuration of physics-based simulation environments (e.g., different simulators and robot models)
Modeling and implementation of contact-rich handling and assembly operations in simulation
Investigation of state-of-the-art foundation models for modeling and generalizing assembly operations
Experience with robotics simulation environments such as Isaac Sim, MuJoCo or Bullet
Strong programming skills in Python, C++ and experience with ROS (Robot Operating System)
Interest or initial hands-on experience with generative AI, especially Large Language Models (LLMs) and Vision-Language Models (VLMs)
Fluent in English
Collaboration with the best students in their field
Remuneration according to the general works agreement for employing assistant staff.
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