Teaching Robots to Learn from Experience with Critics

Researchers from Rice University have proposed a new AI framework called RAG-Modulo, aimed at improving how robots solve complex tasks.

Traditionally, robots struggle with uncertainties in their actions and observations, which makes it difficult for them to perform tasks efficiently, especially over long periods. Current methods often rely on large language models (LLMs), which are good at generating plans for robots. However, they lack one crucial capability: learning from their mistakes.