Towards a cognitive architecture to enable natural language interaction in co-constructive task learning
This work addresses the challenge of enabling more effective human-robot interaction through natural language, but it is incremental as it builds on existing concepts without presenting new experimental results.
The paper tackles the problem of designing a cognitive architecture for natural language interaction in co-constructive task learning, proposing a unified framework by integrating insights from multiple research domains.
This research addresses the question, which characteristics a cognitive architecture must have to leverage the benefits of natural language in Co-Constructive Task Learning (CCTL). To provide context, we first discuss Interactive Task Learning (ITL), the mechanisms of the human memory system, and the significance of natural language and multi-modality. Next, we examine the current state of cognitive architectures, analyzing their capabilities to inform a concept of CCTL grounded in multiple sources. We then integrate insights from various research domains to develop a unified framework. Finally, we conclude by identifying the remaining challenges and requirements necessary to achieve CCTL in Human-Robot Interaction (HRI).