Lifelong Active Inference of Gait Control
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388321" target="_blank" >RIV/68407700:21230/25:00388321 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TNNLS.2025.3579814" target="_blank" >https://doi.org/10.1109/TNNLS.2025.3579814</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2025.3579814" target="_blank" >10.1109/TNNLS.2025.3579814</a>
Alternative languages
Result language
angličtina
Original language name
Lifelong Active Inference of Gait Control
Original language description
Sustaining the robot’s longevity becomes challenging in dynamic deployments characterized by new unknown environments and embodiments outside of the prior knowledge. Hence, the knowledge of robot-environment interactions needs to be continually updated for system adaptation. It can be implemented through self-verification as a continual comparison of predictions with observations using the predictive coding (PC) principle. The principle has been further extended into the active inference control (AIC) in biomimetic robotics to drive the control, state estimation, and model update. However, continually updating one model leads to catastrophic forgetting in the long term. Therefore, we propose an autonomously expanding self-verifying world model (WM) of sensorimotor dynamics utilized in model-based gait control. The model combines PC with the incremental knowledge representation based on the internal model (IM) principle. The proposed method is experimentally validated in virtual and real scenarios, where the hexapod walking robot has to recognize and adapt to leg paralysis and then recognize the recovery. The method generates novel behaviors in real time, improving the performance and outperforming the examined state-of-the-art methods. Furthermore, the robot’s decisions and gained knowledge are interpretable and promise further functional scalability.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20204 - Robotics and automatic control
Result continuities
Project
<a href="/en/project/GC21-33041J" target="_blank" >GC21-33041J: Learning Complex Motion Planning Policies</a><br>
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
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
2162-2388
Volume of the periodical
36
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
19133-19144
UT code for WoS article
001518834100001
EID of the result in the Scopus database
2-s2.0-105009702895