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Towards open and expandable cognitive AI architectures for large-scale multi-agent human-robot collaborative learning
2021
IEEE Access
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 in this paper, targeting to enable the reliable deployment of open, scalable and expandable
doi:10.1109/access.2021.3080517
fatcat:nzxzaxbx2jaf5owuihlxxfizuq