The Need for a Meta-Architecture for Robot Autonomy
This work addresses the problem of reliable long-term autonomy for robotic systems, but it is incremental as it builds on existing principles without introducing a new paradigm.
The paper tackles the challenge of ensuring certifiable dependability in autonomous robots as autonomy levels increase, proposing a generative model of cognitive architectures based on formal models and principles like model-based engineering to address hardware, software, and knowledge faults.
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.