Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards open and expandable cognitive AI architectures for large-scale
  multi-agent human-robot collaborative learning   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.

29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas