SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention Article Swipe
Isabel Leal
,
Krzysztof Choromański
,
Deepali Jain
,
Avinava Dubey
,
Jake Varley
,
Michael S. Ryoo
,
Yao Lu
,
Frederick Liu
,
Vikas Sindhwani
,
Quan Vuong
,
Tamás Sarlós
,
Ken Oslund
,
Karol Hausman
,
Kanishka Rao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.01990
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.01990
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment. SARA-RT relies on the new method of fine-tuning proposed by us, called up-training. It converts pre-trained or already fine-tuned Transformer-based robotic policies of quadratic time complexity (including massive billion-parameter vision-language-action models or VLAs), into their efficient linear-attention counterparts maintaining high quality. We demonstrate the effectiveness of SARA-RT by speeding up: (a) the class of recently introduced RT-2 models, the first VLA robotic policies pre-trained on internet-scale data, as well as (b) Point Cloud Transformer (PCT) robotic policies operating on large point clouds. We complement our results with the rigorous mathematical analysis providing deeper insight into the phenomenon of SARA.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.01990
- https://arxiv.org/pdf/2312.01990
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389364340
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4389364340Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.01990Digital Object Identifier
- Title
-
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust AttentionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-04Full publication date if available
- Authors
-
Isabel Leal, Krzysztof Choromański, Deepali Jain, Avinava Dubey, Jake Varley, Michael S. Ryoo, Yao Lu, Frederick Liu, Vikas Sindhwani, Quan Vuong, Tamás Sarlós, Ken Oslund, Karol Hausman, Kanishka RaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.01990Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.01990Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.01990Direct OA link when available
- Concepts
-
Robotics, Artificial intelligence, Transformer, Computer science, Software deployment, Robot, Control engineering, Engineering, Electrical engineering, Software engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.emerging | 15 |
| abstract_inverted_index.on-robot | 24 |
| abstract_inverted_index.paradigm | 11 |
| abstract_inverted_index.policies | 47, 88, 102 |
| abstract_inverted_index.proposed | 34 |
| abstract_inverted_index.quality. | 66 |
| abstract_inverted_index.recently | 80 |
| abstract_inverted_index.rigorous | 114 |
| abstract_inverted_index.speeding | 74 |
| abstract_inverted_index.Attention | 4 |
| abstract_inverted_index.challenge | 16 |
| abstract_inverted_index.efficient | 61 |
| abstract_inverted_index.operating | 103 |
| abstract_inverted_index.providing | 117 |
| abstract_inverted_index.quadratic | 49 |
| abstract_inverted_index.(SARA-RT): | 8 |
| abstract_inverted_index.(including | 52 |
| abstract_inverted_index.addressing | 13 |
| abstract_inverted_index.complement | 109 |
| abstract_inverted_index.complexity | 51 |
| abstract_inverted_index.fine-tuned | 44 |
| abstract_inverted_index.introduced | 81 |
| abstract_inverted_index.phenomenon | 122 |
| abstract_inverted_index.Transformer | 99 |
| abstract_inverted_index.demonstrate | 68 |
| abstract_inverted_index.deployment. | 25 |
| abstract_inverted_index.fine-tuning | 33 |
| abstract_inverted_index.maintaining | 64 |
| abstract_inverted_index.pre-trained | 41, 89 |
| abstract_inverted_index.Transformers | 7, 21 |
| abstract_inverted_index.counterparts | 63 |
| abstract_inverted_index.mathematical | 115 |
| abstract_inverted_index.up-training. | 38 |
| abstract_inverted_index.Self-Adaptive | 2 |
| abstract_inverted_index.effectiveness | 70 |
| abstract_inverted_index.internet-scale | 91 |
| abstract_inverted_index.linear-attention | 62 |
| abstract_inverted_index.Transformer-based | 45 |
| abstract_inverted_index.billion-parameter | 54 |
| abstract_inverted_index.vision-language-action | 55 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 14 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |