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Robots learn to anticipate chaos, but still fail to read a decidedly human signal

Robots learn to anticipate chaos, but still fail to read a decidedly human signal

Researchers at Cornell University are exploring the integration of artificial intelligence into robotics to enhance social intelligence, enabling robots to interpret facial expressions, anticipate human needs, and interact effectively within societal contexts. In a recent study, the team evaluated vision language models (VLMs)—AI systems capable of processing and generating both visual and linguistic data. The research focused on assessing these models' ability to predict outcomes in tense scenarios depicted in short videos, such as a toddler precariously carrying an overflowing mug of coffee. This investigation aims to advance the development of robots that can better understand and respond to human emotions and behaviors, ultimately improving their functionality in everyday environments.

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