Skip to content
AI News HubLIVE
Source content · Analysis pending2 min read

In-Context Learning for Robots: Methods and Applications

Summary

arXiv:2609.36012v1 Announce Type: new Abstract: General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this process by using demonstrations and interaction to direct existing competence with neural parameters held fixed during deployment. We organize this literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution. Comparing these interfaces clarifies their transfer assumptions and the roles of training, correspondence, and memory in making context useful. Across manipulation and navigation, we…

SourcearXiv RoboticsAuthor: Haojian Huang, Zexi Li, Junhao Guo, Yehang Zhang, Wenxuan Peng, Bohan Zhou, Weilin Ruan, Leyi Wu, Chenxu Wang, Jianchong Su, Binghui Xie, Wosong Chen, Yingjie Xu, Tianhao Zhou, Suzeyu Chen, Pukun Zhao, Jiaqi He, Xinyi Li, Runze Li, Peiran Dong, Shaoxiang Dang, Jing Huang, Yingbing Chen, Yifan Chang, Tianyi Zhang, Shiyuan Deng, Haozhi Wang, Yangkai Wei, Wenqian Li, Han Yang, Kaiwen Zhou, Huaping Liu, James Cheng, Rui Shao, Donglin Wang, Yaochu Jin, Jianye Hao, Ying-Cong Chen, Yinchuan Li
In-Context Learning for Robots: Methods and Applications
Report an error

The correction channel is not available yet. You can copy the article reference below for later.

Correction instructions
Read article

[Submitted on 28 Sep 2026]

Title:In-Context Learning for Robots: Methods and Applications

View a PDF of the paper titled In-Context Learning for Robots: Methods and Applications, by Haojian Huang and 38 other authors

View PDF HTML (experimental)

Abstract:General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-context learning (ICL) for robots supports this process by using demonstrations and interaction to direct existing competence with neural parameters held fixed during deployment. We organize this literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution. Comparing these interfaces clarifies their transfer assumptions and the roles of training, correspondence, and memory in making context useful. Across manipulation and navigation, we examine how these mechanisms preserve taught requirements as objects, environments, and execution conditions change. This analysis links method design to evaluation practices that distinguish responsiveness to teaching, physical transfer, and benefits from retained experience. The resulting agenda connects compositional task acquisition and faithful transfer with physical recursive self-improvement, in which experience improves the ability to learn subsequent tasks.

Comments: 100 pages, 26 figures, 25 tables. Project page: this https URL ; Code and literature: this https URL

Subjects:

Robotics (cs.RO); Machine Learning (cs.LG)

Cite as: arXiv:2609.36012 [cs.RO]

(or arXiv:2609.36012v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.36012

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Haojian Huang [view email] [v1] Mon, 28 Sep 2026 18:00:34 UTC (28,958 KB)

Full-text links:

Access Paper:

View a PDF of the paper titled In-Context Learning for Robots: Methods and Applications, by Haojian Huang and 38 other authors

View PDF

HTML (experimental)

TeX Source

view license

Ancillary-file links:

Ancillary files (details):

Supplement-S1/CODEBOOK.md

Supplement-S1/README.md

Supplement-S1/REPORTED-COMPARISONS.md

Supplement-S1/annual-counts.csv

Supplement-S1/checksums.sha256

Supplement-S1/codebook.json

Supplement-S1/corpus.csv

Supplement-S1/corpus.json

Supplement-S1/coverage-counts.csv

Supplement-S1/family-counts.csv

Supplement-S1/references.csv

Supplement-S1/references.json

Supplement-S1/reported-comparisons.csv

Supplement-S1/reported-comparisons.json

Supplement-S1/reproduce.py

Supplement-S1/role-counts.csv

Supplement-S1/summary.json

(12 additional files not shown)

Current browse context:

cs.RO

new | recent | 2026-09

Change to browse by:

cs cs.LG

References & Citations

NASA ADS

Google Scholar

Semantic Scholar

Loading...

Data provided by:

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

Key points and analysis

Article intelligence

EngineersAdvanced

Key points

  • AI generation is temporarily unavailable; this entry was preserved with deterministic fallback metadata.
  • arXiv:2609.36012v1 Announce Type: new Abstract: General-purpose robots must infer what a new task requires and translate that understanding into appropriate physical action. In-co…

Highlights and analysis are generated automatically and may contain errors. Check the original source.

More recent stories on this topic · Agents