Unsupervised Discovery of Behavioral Primitives from Sensorimotor Dynamic Functional Connectivity
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00387029" target="_blank" >RIV/68407700:21230/25:00387029 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICDL63968.2025.11204389" target="_blank" >https://doi.org/10.1109/ICDL63968.2025.11204389</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICDL63968.2025.11204389" target="_blank" >10.1109/ICDL63968.2025.11204389</a>
Alternative languages
Result language
angličtina
Original language name
Unsupervised Discovery of Behavioral Primitives from Sensorimotor Dynamic Functional Connectivity
Original language description
The movements of both animals and robots give rise to streams of high-dimensional motor and sensory information. Imagine the brain of a newborn or the controller of a baby humanoid robot trying to make sense of unprocessed sensorimotor time series. Here, we present a framework for studying the dynamic functional connectivity between the multimodal sensory signals of a robotic agent to uncover an underlying structure. Using instantaneous mutual information, we capture the time-varying functional connectivity (FC) between proprioceptive, tactile, and visual signals, revealing the sensorimotor relationships. Using an infinite relational model, we identified sensorimotor modules and their evolving connectivity. To further interpret these dynamic interactions, we employed non-negative matrix factorization, which decomposed the connectivity patterns into additive factors and their corresponding temporal coefficients. These factors can be considered the agent’s motion primitives or movement synergies that the agent can use to make sense of its sensorimotor space and later for behavior selection. In the future, the method can be deployed in robot learning as well as in the analysis of human movement trajectories or brain signals.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE International Conference on Development and Learning (ICDL)
ISBN
979-8-3315-4343-3
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
—
Publisher name
IEEE Conference Publications
Place of publication
Piscataway
Event location
Praha
Event date
Sep 16, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—