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Faculty of Electrical Engineering and Information Technology

Differentiable Constrained Imitation Learning for Robot Motion Planning and Control

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In the age of large language models, imposing constraints on learning-based approaches becomes even more critical. Therefore, we developed a method to integrate safety constraints into imitation learning approaches for general robotic applications.

Publikation at Arxiv