Year: 2025
Type: preprint
Abstract: We introduce STRING: Separable Translationally Invariant Position Encodings. STRING extends Rotary Position Encodings, a recently proposed and widely used algorithm in large language models, via a unifying theoretical framework. Importantly, STRING still provides exact translation invariance, including token coordinates of arbitrary dimensionality, whilst maintaining a low computational footprint. These properties are especially important in robotics, where efficient 3D token representation is key. We integrate STRING into Vision Transformers with RGB(-D) inputs (color plus optional depth), showing substantial gains, e.g. in open-vocabulary object detection and for robotics controllers. We complement our experiments with a rigorous mathematical analysis, proving the universality of our methods.
Source: arXiv (Cornell University)
Authors: Schenck, Connor, Reid, Isaac, Jacob, Mithun George, Bewley, Alex, Ainslie, Joshua, Rendleman, David, Jain, Deepali, Sharma, Mohit, Dubey, Avinava, Wahid, Ayzaan, Singh, Sumeet, Wagner, René, Ding, Tianli, Fu, Chuyuan, Byravan, Arunkumar, Varley, Jake, Gritsenko, Alexey, Minderer, Matthias, Kalashnikov, Dmitry, Tompson, Jonathan, Sindhwani, Vikas, Choromanski, Krzysztof
Institutions: University of Cambridge, Google (United States)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4407186933