Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning

TL;DR

This paper presents a novel federated learning-based cognitive architecture for multi-agent human-robot collaborative learning, enabling scalable, open, and long-term autonomous robotic systems in complex environments.

Contribution

It introduces a new FL-based formalism extending LfD for large-scale multi-agent settings, incorporating human interaction and multi-level task dependencies.

Findings

Demonstrated the framework in a real-world industrial CRM recovery case

Extended LfD paradigm to support multi-agent collaborative learning

Integrated human-in-the-loop and advanced AI methodologies

Abstract

Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a real-world industrial case study for agile production-based Critical Raw Materials (CRM) recovery.