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Summary

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022), by Michael Ahn, Anthony Brohan, Noah Brown, and many coauthors, is at https://arxiv.org/abs/2204.01691. Their method uses pretrained skills to constrain the model to propose natural language actions that are both feasible and contextually appropriate. In this setup, the robot supplies a form of hands and eyes, while the language model supplies high-level knowledge about the procedure, and value functions attached to the skills provide grounding in the physical environment. They evaluate the approach on real-world tasks with a mobile manipulator. The authors say 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. They evaluate the method on a number of real-world robotic tasks, and they report that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project website named in the abstract is https://say-can.github.io/. Title, authors, abstract, and comments were taken from the arXiv abs page. The version is v2 (16 Aug 2022), submitted 4 Apr 2022.

Claims

Pointer notes do not quote the paper. See Verification and Sources for the link and license.

Verification

• Format: pointer note only. The paper is not reproduced here.
• Why it matters: It matters as a key paper connecting language models to physical action by constraining plans to what a particular robot can actually do.
• Reproduced: not reproduced (2026-11-11).
• Related work: Related notes already in the library include ReAct and CodeAct.
• Limits: This is a pointer note only. It summarizes the grounding idea at a high level and does not describe robot control procedures. The project site is named in the paper comments. Project site named in the comments: https://say-can.github.io/.
• License: arXiv default (non-exclusive distribution license) on the abs page (2026-11-11; http://arxiv.org/licenses/nonexclusive-distrib/1.0/). Submitted 4 Apr 2022 (v1); last revised 16 Aug 2022 (v2). Sources: [S1] https://arxiv.org/abs/2204.01691, v2 (16 Aug 2022). checked_on: 2026-11-11. Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it. The note itself is CC BY 4.0.

When to apply

Use when you need the original paper on grounding language-model plans in a robot's learned skills and affordances.

Facts

• id: F1 claim: 'The paper proposes grounding language models with pretrained skills so that proposed natural language actions are feasible and contextually appropriate for a robot.' confidence: medium date: 2026-11-11 sources: [S1] license: CC-BY-4.0
• id: F2 claim: 'Value functions associated with those skills are used to connect high-level language-model knowledge to a particular physical environment.' confidence: medium date: 2026-11-11 sources: [S1] license: CC-BY-4.0
• id: F3 claim: 'The authors evaluate the method on real-world robotic tasks with a mobile manipulator, and they point to https://say-can.github.io/.' confidence: medium date: 2026-11-11 sources: [S1] license: CC-BY-4.0

Sources

• [S1] arXiv 2204.01691, https://arxiv.org/abs/2204.01691. Paper license: arXiv default (non-exclusive distribution license). v2 (16 Aug 2022). Accessed 2026-11-11. This note links to the paper and does not reproduce it.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

@misc{iil_note_pp6etbey,
  author = {{'iinventedlight (AI-assisted)'}},
  title = {{Do As I Can, Not As I Say: Grounding Language in Robotic Affordances}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-11},
  url = {https://iinventedlight.com/library/notes/2204-01691-saycan/},
  note = {Permanent ID iil:note:pp6etbey. License: CC-BY-4.0.},
}

Markdown

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