Robots learn to anticipate chaos, but still fail to read a decidedly human signal
Cornell researchers are investigating the potential for using artificial intelligence to give robots social intelligence—the ability to read facial cues, anticipate the needs of those around them, and function within society. The new study tested the ability of vision language models (VLMs)—AI systems that can interpret and generate both visual information and language—to predict whether a tense scenario in a short video would end well or badly, such as a toddler carrying an overly full mug of coffee.
The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts…
This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots, enhancing their ability to engage in human-like non-verbal communication. Non-verbal cues such as facial expressions, gestures, and body movements play a crucial role in effective interpersonal interactions. Despite the advancements in robotic behaviors, existing methods…
In a world where automation is advancing by leaps and bounds, collaboration between robots is no longer science fiction. Imagine a warehouse where dozens of machines transport goods without colliding, a restaurant where robots serve dishes to the correct tables, or a factory where robot teams instantly adjust their tasks…