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Meta unveils AI tools to give robots a human touch in physical world

Meta unveils three tools and benchmarks to enhance robot touch perception, dexterity, and human-robot collaboration in real-world settings.

The release comes as advances in foundational models have renewed interest in robotics, and AI companies are gradually expanding their race from the digital realm to the physical world. The classic approach to incorporating vision-based tactile sensors in robot tasks is to use labeled data to train custom models that can predict useful states. According to the researchers’ experiments, Sparsh gains an average 95.1% improvement over task- and sensor-specific end-to-end models under a limited labeled data budget.

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