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SAILOR: perceptual anchoring for robotic cognitive architectures
Miguel Á González-Santamarta1, Francisco J Rodrıguez-Lera2, Vicente Matellan-Olivera2
1Robotics Group, Department of Mechanic Engineering, Computer and Aerospace Sciences, University of León, 24006, León, Spain. mgons@unileon.es.
Scientific Reports
|January 2, 2025
Summary
This study introduces SAILOR, a framework for symbolic anchoring in robotics. SAILOR links robot perception to symbolic knowledge, addressing a key challenge in cognitive robotics.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Symbolic anchoring is crucial for robots to link sensor data with symbolic knowledge.
- Transforming sub-symbolic sensor data into symbolic knowledge remains an open problem in cognitive robotics.
Purpose of the Study:
- To present SAILOR, a novel framework for symbolic anchoring integrated into ROS 2.
- To enable robots to maintain the link between symbolic and perceptual data over time.
Main Methods:
- SAILOR employs a semantic world modeling approach.
- Utilizes deep learning-based skills: object recognition and a matching function.
- Integrates with MERLIN2, a hybrid cognitive architecture for ROS 2 robots.
Main Results:
- The SAILOR framework successfully links symbolic data with perceptual data in real robots.
- Demonstrated effectiveness through validation on public datasets and a real-world scenario.
Conclusions:
- SAILOR provides a robust solution for symbolic anchoring in cognitive robots.
- Enhances robots' ability to acquire and maintain symbolic knowledge from sensory input.
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