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Teaching robots their limits

The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
  • illustration of robot arm sorting items
    Depiction of a robot arm encountering obstacles while moving items on a table top
    PRoC3S team

Recently, robotics researchers have used large language models (LLMs) to develop a sequence of specific steps to achieve simple tasks. A team including Professors Lozano-Pérez and Kaelbling have published a paper that explores LLM planning for continuously parameterized skills that avoid violations of a set of kinematic, geometric, and physical constraints. 

The team gives an example of asking a robot to clean a kitchen — if the robot doesn't understand the parameters and limitations of the space it is in, there are likely to be broken dishes or a wet floor. The LLM is used to propose steps to complete a task, then examines those steps to determine the feasibility. If there are problems with any individual step, it generates another proposal until it has a working process.

Eventually, they hope that robots will understand that a command like "clean the kitchen" means that dirty dishes go in the dishwasher, clean cups belong in the cupboard, and a mop should be used on the floor.