  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 reasonable narrative, but it may not be
applicable to a particular agent, such as a robot, that needs to perform this
task in a particular environment. We propose to provide real-world grounding by
means of pretrained skills, which are used to constrain the model to propose
natural language actions that are both feasible and contextually appropriate.
The robot can act as the language model's "hands and eyes," while the language
model supplies high-level semantic knowledge about the task. We show how
low-level skills can be combined with large language models so that the
language model provides high-level knowledge about the procedures for
performing complex and temporally-extended instructions, while value functions
associated with these skills provide the grounding necessary to connect this
knowledge to a particular physical environment. We evaluate our method on a
number of real-world robotic tasks, where we show the need for real-world
grounding and that this approach is capable of completing long-horizon,
abstract, natural language instructions on a mobile manipulator. The project's
website and the video can be found at https://say-can.github.io/.
