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Mechanical intelligence for learning embodied sensor-object relationships
Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine le...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287329/ https://www.ncbi.nlm.nih.gov/pubmed/35840570 http://dx.doi.org/10.1038/s41467-022-31795-2 |
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author | Prabhakar, Ahalya Murphey, Todd |
author_facet | Prabhakar, Ahalya Murphey, Todd |
author_sort | Prabhakar, Ahalya |
collection | PubMed |
description | Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model—without requiring domain knowledge, human input, or previously existing data—using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. |
format | Online Article Text |
id | pubmed-9287329 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92873292022-07-17 Mechanical intelligence for learning embodied sensor-object relationships Prabhakar, Ahalya Murphey, Todd Nat Commun Article Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsupervised learning problem. Current machine learning methods process new sensory data using prior knowledge defined by either domain knowledge or datasets. When datasets are not available, data acquisition is needed, though automating exploration in support of learning is still an unsolved problem. Here we develop a method that enables agents to efficiently collect data for learning a predictive sensor model—without requiring domain knowledge, human input, or previously existing data—using ergodicity to specify the data acquisition process. This approach is based entirely on data-driven sensor characteristics rather than predefined knowledge of the sensor model and its physical characteristics. We learn higher quality models with lower energy expenditure during exploration for data acquisition compared to competing approaches, including both random sampling and information maximization. In addition to applications in autonomy, our approach provides a potential model of how animals use their motor control to develop high quality models of their sensors (sight, sound, touch) before having knowledge of their sensor capabilities or their surrounding environment. Nature Publishing Group UK 2022-07-15 /pmc/articles/PMC9287329/ /pubmed/35840570 http://dx.doi.org/10.1038/s41467-022-31795-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Prabhakar, Ahalya Murphey, Todd Mechanical intelligence for learning embodied sensor-object relationships |
title | Mechanical intelligence for learning embodied sensor-object relationships |
title_full | Mechanical intelligence for learning embodied sensor-object relationships |
title_fullStr | Mechanical intelligence for learning embodied sensor-object relationships |
title_full_unstemmed | Mechanical intelligence for learning embodied sensor-object relationships |
title_short | Mechanical intelligence for learning embodied sensor-object relationships |
title_sort | mechanical intelligence for learning embodied sensor-object relationships |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9287329/ https://www.ncbi.nlm.nih.gov/pubmed/35840570 http://dx.doi.org/10.1038/s41467-022-31795-2 |
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