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Liquid pouring is a critical, high-precision task in laboratory automation and pharmaceutical manufacturing. Existing imitation learning methods struggle with the high demands for accuracy and robustness in contact-rich pouring tasks. This thesis presents an extension of the diffusion policy framework by integrating force feedback for a dual-arm robot pouring approach. Unlike traditional methods,
