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DeFM: A Foundation Model for Learning Robotic Representations from Depth Images

DeFM is a foundation model trained entirely on depth images through self-supervised learning. It uses a DINO-style self-distillation objective to learn geometric and semantic representations from a dataset of 60 million depth images that can generalize across different environments, tasks, and sensors.

Part of the event: DeFM: A Robot Foundation Model Trained Only on Depth Images

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