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Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
[article]
2022
arXiv
pre-print
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a
arXiv:2204.01691v2
fatcat:a73us6inhzerjjt5jk6a2tpb5i