Towards Conscious Service Robots

TL;DR

This paper explores how integrating human cognitive architectures, including System 1 and System 2 processes, can enable service robots to adapt, generalize, and self-monitor in complex, real-world environments.

Contribution

It proposes a framework for developing conscious service robots by incorporating causal reasoning, working memory, planning, and metacognition inspired by human cognition.

Findings

Humans adapt quickly due to cognitive architecture features.

Incorporating causal models and metacognition enhances robot flexibility.

Self-monitoring reduces errors and improves safety in robots.

Abstract

Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain's System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors.