Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition

TL;DR

This paper presents a cognitive architecture that combines perceptual processes with working memory to improve self-supervised multi-modal human recognition in robots, enhancing their autonomy in dynamic environments.

Contribution

It introduces a novel cognitive architecture integrating working memory with perception to enable autonomous, self-supervised learning for human recognition in robots.

Findings

Architecture effectively organizes sensory data for recognition.

Improves robot autonomy in dynamic, real-world scenarios.

Demonstrates promising results in multi-modal human recognition.

Abstract

The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions, especially in scenarios like education, care-giving, and rehabilitation. Faces and voices constitute two important sources of information to enable artificial systems to reliably recognize individuals. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. However, when those networks are applied to different and unprecedented scenarios not included in the training set, they can suffer a drop in performance. For example, with robotic platforms in ever-changing and realistic environments, where always new sensory evidence is acquired, the performance of those models degrades. One solution is to make robots learn from their first-hand sensory data with self-supervision. This allows coping with the inherent variability of the data gathered in realistic and interactive contexts. To this aim, we propose a cognitive architecture integrating low-level perceptual processes with a spatial working memory mechanism. The architecture autonomously organizes the robot's sensory experience into a structured dataset suitable for human recognition. Our results demonstrate the effectiveness of our architecture and show that it is a promising solution in the quest of making robots more autonomous in their learning process.