Engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team's method connects robot motion data with the common sense knowledge of large language models, or LLMs.
Engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team's method connects robot motion data with the common sense knowledge of large language models, or LLMs.
Now MIT engineers are aiming to give robots a bit of common sense when faced with situations that push them off their trained path. They've developed a method that connects robot motion data with the"common sense knowledge" of large language models, or LLMs. Wang and his colleagues detail their new approach in a study they will present at the International Conference on Learning Representations in May. The study's co-authors include EECS graduate students Tsun-Hsuan Wang and Jiayuan Mao, Michael Hagenow, a postdoc in MIT's Department of Aeronautics and Astronautics , and Julie Shah, the H.N. Slater Professor in Aeronautics and Astronautics at MIT.
"The grounding classifier facilitates this dialogue between what the robot is doing in the physical space and what the LLM knows about the subtasks, and the constraints you have to pay attention to within each subtask," Wang explains.
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