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Danny Driess

Danny Driess has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Vision & language, Tokenization & segmentation; often with Brian Ichter, Andy Zeng, Chelsea Finn.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 22 · 60 works · 2,672 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 686 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #6339

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of StuttgartStuttgart · Germany2017–2021 · 15 papers🎓Technische Universität BerlinBerlin · Germany2021–2024 · 9 papers🔬Max Planck Institute for Intelligent SystemsStuttgart · Germany2020–2021 · 8 papers🏢Google DeepMindLondon · United Kingdom2023–2025 · 4 papers🏢GoogleMountain View · United States2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksEngineering · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Vision and Pattern Recognition · 1 workControl and Systems Engineering · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workTokenization & segmentation · 1 workVision & language · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Subword tokenization · 1 workTokenization · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FAST: Efficient Action Tokenization for Vision-Language-Action Models (2025 · 123 citations)
    The authors investigate how to convert continuous robot control signals into discrete tokens for autoregressive VLA models.
  2. Large Language Models as General Pattern Machines (2023 · 88 citations)
    The paper asks whether pre-trained LLMs, used with no fine-tuning, can act as generic engines for recognizing and continuing abstract, non-linguistic patterns, and whether this skill transfers to low-level robot control.

All works

7 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. PaLM-E: An Embodied Multimodal Language Model ↗arXiv:2303.03378 · no summary yetDanny Driess et al.2023 · 700 citations
  2. Towards Generalist Biomedical AI ↗NEJM AI · no summary yetTao Tu et al.2024 · 532 citations
  3. Open X-Embodiment: Robotic Learning Datasets and RT-X Models ↗arXiv:2310.08864 · no summary yetEmbodiment Collaboration et al.2023 · 204 citations
  4. FAST: Efficient Action Tokenization for Vision-Language-Action ModelsarXiv:2501.09747Karl Pertsch, Kyle Stachowicz, Brian Ichter, et al.2025 · 123 citations
  5. Large Language Models as General Pattern MachinesarXiv:2307.04721Suvir Mirchandani, Fei Xia, Pete Florence, et al.2023 · 88 citations
  6. Foundation Models in Robotics: Applications, Challenges, and the Future ↗arXiv (Cornell University) · no summary yetRoya Firoozi et al.2023 · 33 citations
  7. PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs ↗arXiv (Cornell University) · no summary yetSoroush Nasiriany et al.2024 · 21 citations

 Co-authors here

7 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 686 times (of the 149,439 papers whose bibliographies we read); 120 citing papers with a page here →.

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