Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

TL;DR

This paper introduces a method that combines large language models with pretrained robotic skills to enable robots to understand and execute complex natural language instructions effectively in real-world environments.

Contribution

The paper presents a novel framework that grounds language models with real-world robotic skills, improving task feasibility and contextual appropriateness for robotic applications.

Findings

Successfully completes long-horizon, natural language instructions in real-world robotic tasks.

Demonstrates the importance of real-world grounding for language-based robotic control.

Shows that combining language models with skills enhances task execution accuracy.

Abstract

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/.