The Need for a Meta-Architecture for Robot Autonomy

TL;DR

This paper advocates for a meta-architecture in robot autonomy that integrates formal models of cognition, reasoning, and dependability to enhance long-term autonomous robot reliability and certifiability.

Contribution

It proposes a generative, model-based cognitive architecture framework that combines dependability, knowledge processing, and meta-reasoning for autonomous robots.

Findings

Highlights the importance of formal models for dependability.

Suggests integrating cognitive functions for certifiable autonomy.

Proposes a generative architecture based on model-based engineering.

Abstract

Long-term autonomy of robotic systems implicitly requires dependable platforms that are able to naturally handle hardware and software faults, problems in behaviors, or lack of knowledge. Model-based dependable platforms additionally require the application of rigorous methodologies during the system development, including the use of correct-by-construction techniques to implement robot behaviors. As the level of autonomy in robots increases, so do the cost of offering guarantees about the dependability of the system. Certifiable dependability of autonomous robots, we argue, can benefit from formal models of the integration of several cognitive functions, knowledge processing, reasoning, and meta-reasoning. Here we put forward the case for a generative model of cognitive architectures for autonomous robotic agents that subscribes to the principles of model-based engineering and certifiable dependability, autonomic computing, and knowledge-enabled robotics.