Massachusetts Institute of Technology engineers are developing software for household robots that uses large language models to help them acquire “common sense.” They believe this will enable such robots to complete tasks when interrupted or misstepping.
MIT’s new technique allows a robot to break down various household tasks into smaller sub-tasks and adapt to any disruptions within a sub-task. This enables the robot to continue a task without starting from the beginning or requiring engineers to manually program solutions for every possible failure.“Imitation learning is a mainstream approach enabling household robots. But suppose a robot is blindly mimicking a human’s motion trajectories.
To make the robot more human-like, they may repeat this process several times to create demonstrations for the robot to imitate. “But the human demonstration is one long, continuous trajectory,” Wang says. The team’s obvious choice was deep learning models, specifically LLMs. These can process vast amounts of text data and establish connections between words, sentences, and paragraphs.This allows the models to generate new sentences based on what they have learned about the likelihood of certain words following others.
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