AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
[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?)