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Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence–assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human's natural language commands.

In a study to be presented at the annual Conference on Robot Learning (CoRL 2026), held in Austin, Texas, Nov. 9–12, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion planning.

With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task.

The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type "Don't spill on the laptop!" In response, the robot will reason about why it should avoid the laptop.

For this, FLEX incorporates a large language model (LLM) —a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions and web content, which the LLM uses to identify larger trends, patterns and generalizations.

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