JAKUB PAWLAK

"EMBODIED DEEP LEARNING"

Deep Learning methods have evolved significantly across various data regimes in the field's history. From early approaches that utilized simple toy datasets to contemporary methods fueled by complex, multi-modal internet-scale data, the progression has been remarkable. This talk investigates the potential of leveraging robotics as a new frontier for data collection, specifically through the physical world. The combination of unsupervised learning with robotics may potentially increase the training data available by orders of magnitude. We delve into the possibilities of employing momentum-based unsupervised learning techniques for learning from interactions with the environment.

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