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PITTSBURGH — Babies and toddlers learn by exploring their surroundings and now robots can too. In a groundbreaking collaboration between Carnegie Mellon University and Meta, scientists have drawn inspiration from the way infants learn to create an innovative approach to teaching robots. The result is RoboAgent, an artificial intelligence agent designed to emulate a toddler’s learning process and acquire manipulation skills equivalent to a three-year-old child.
“We aimed to create a single AI agent capable of a wide range of skills in novel situations, similar to how human babies learn,” explains Vikash Kumar, from Carnegie Mellon’s School of Computer Science’s Robotics Institute. “RoboAgent leverages passive observations and limited active play, just like infants who keenly watch, imitate, and replay to learn.”
The unique learning architecture of RoboAgent is the core of its effectiveness and efficiency. It employs temporal chunks of movements to make decisions, diverging from the traditional per-time step approach. This innovative policy structure facilitates reasoning even with limited experiences, enabling the agent to act according to specified goals.
RoboAgent’s learning process draws inspiration from the way children accumulate knowledge. Just as parents guide their offspring, researchers teleoperated the robot to provide valuable self-experiences. However, RoboAgent’s learning scope goes beyond its immediate environment.
“To overcome limitations, RoboAgent learns from internet videos, similar to how babies acquire behaviors by observing their surroundings,” says Mohit Sharma, a Ph.D. student in robotics. “These videos help RoboAgent learn how humans interact with objects and utilize skills to complete tasks. It extracts valuable lessons from different scenarios and applies them to new challenges.”
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