In this work, we design a new learning system based on large language model (LLM), OLAF, that allows everyday users to teach a robot using verbal corrections when the robot makes mistakes, e.g., by saying “Stop what you’re doing. ![]() ![]() The ability to learn and refine behavior after deployment has become ever more important for robots as we design them to operate in unstructured environments like households.
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