SAILOR: perceptual anchoring for robotic cognitive architectures
2025
SAILOR: A Framework for Symbolic Anchoring in Robotics
publication
10 minutes
Evidence: high
Author Information
Author(s): González-Santamarta Miguel Á., Rodrıguez-Lera Francisco J., Matellan-Olivera Vicente, del Castillo Virginia Riego, Sánchez-González Lidia
Primary Institution: University of León
Hypothesis
How can robots maintain the correspondence between symbolic data and sensor data in cognitive architectures?
Conclusion
The SAILOR framework successfully integrates symbolic anchoring into ROS 2, improving real-time knowledge maintenance in robots.
Supporting Evidence
- SAILOR integrates symbolic anchoring into ROS 2 for real-time knowledge maintenance.
- The framework uses deep learning for object recognition and matching.
- SAILOR was validated using public datasets and real-world scenarios.
Takeaway
SAILOR helps robots understand their surroundings by linking what they see with what they know, like matching a toy with its name.
Methodology
The study developed a framework that combines object recognition and a matching function to maintain symbolic knowledge in robots.
Digital Object Identifier (DOI)
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