# Towards a cognitive architecture to enable natural language interaction in co-constructive task learning

arXiv: https://arxiv.org/abs/2503.23760 · HTML: https://mlsanity.com/paper/2503.23760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 57 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)
- [Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models](https://mlsanity.com/paper/2309.04316/) (2023)
- [A Roadmap towards Machine Intelligence](https://mlsanity.com/paper/1511.08130/) (2015)
- [A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX](https://mlsanity.com/paper/2206.02241/) (2022)
- [Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents](https://mlsanity.com/paper/2011.11972/) (2020)
- [EMISSOR: A platform for capturing multimodal interactions as Episodic Memories and Interpretations with Situated Scenario-based Ontological References](https://mlsanity.com/paper/2105.08388/) (2021)
- [CASPER: Cognitive Architecture for Social Perception and Engagement in Robots](https://mlsanity.com/paper/2209.01012/) (2022)
- [From Interactive to Co-Constructive Task Learning](https://mlsanity.com/paper/2305.15535/) (2023)

## Abstract (arXiv)

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).