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This invention describes a system that helps robots learn better by improving the quality of their training data. Many robots send their learning examples, which include data from their sensors (like camera images) and what they've classified (like identifying an object). The system then checks how good these examples are by comparing them to each other. It picks only the best examples to train a central machine learning model, ensuring the robots learn from reliable information.
Why it matters: Filed before the widespread adoption of highly performant foundation models capable of generating robust embeddings on edge devices. These advancements make the core data quality assessment and subsequent training significantly more effective and scalable now.
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