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SAILOR: perceptual anchoring for robotic cognitive architectures

  • 0Robotics Group, Department of Mechanic Engineering, Computer and Aerospace Sciences, University of León, 24006, León, Spain. mgons@unileon.es.
Scientific Reports +

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January 2, 2025

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January 2, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

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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