SAILOR: Perceptual Anchoring For Robotic Cognitive Architectures

TL;DR

SAILOR introduces a framework for symbolic anchoring in robotics, linking perceptual data to symbolic knowledge over time using deep learning, thereby enhancing robot intelligence within the ROS 2 ecosystem.

Contribution

It presents a novel perceptual anchoring framework, SAILOR, integrating deep learning skills for object recognition and matching in ROS 2-based robotic architectures.

Findings

Framework successfully maintains symbolic-perceptual links over time.

Integrates with MERLIN2 cognitive architecture.

Enhances robot understanding and interaction capabilities.

Abstract

Symbolic anchoring is a crucial problem in the field of robotics, as it enables robots to obtain symbolic knowledge from the perceptual information acquired through their sensors. In cognitive-based robots, this process of processing sub-symbolic data from real-world sensors to obtain symbolic knowledge is still an open problem. To address this issue, this paper presents SAILOR, a framework for providing symbolic anchoring in the ROS 2 ecosystem. SAILOR aims to maintain the link between symbolic data and perceptual data in real robots over time, increasing the intelligent behavior of robots. It provides a semantic world modeling approach using two deep learning-based sub-symbolic robotic skills: object recognition and matching function. The object recognition skill allows the robot to recognize and identify objects in its environment, while the matching function enables the robot to decide if new perceptual data corresponds to existing symbolic data. This paper provides a description of the proposed method and the development of the framework, as well as its integration in MERLIN2 (a hybrid cognitive architecture fully functional in robots running ROS 2).