Data Generation Challenges
The conversation delves into the complexities of generating synthetic data for robotic processes, particularly in metal forming. While simulations are a potential solution, they often prove to be slow and resource-intensive. The need for accurate modeling of physical phenomena like deformation and friction presents a significant hurdle, leading to the realization that capturing real-life data may sometimes be more efficient than extensive simulations.In this clip
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The AI Podcast
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