# A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX

arXiv: https://arxiv.org/abs/2206.02241 · HTML: https://mlsanity.com/paper/2206.02241/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 5 Jun 2022.

## Builds on (most cited first, 6 of 6)

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [STORM: An Integrated Framework for Fast Joint-Space Model-Predictive Control for Reactive Manipulation](https://mlsanity.com/paper/2104.13542/) (2021)
- [A Survey of Knowledge Representation in Service Robotics](https://mlsanity.com/paper/1807.02192/) (2018)
- [Deep Episodic Memory: Encoding, Recalling, and Predicting Episodic Experiences for Robot Action Execution](https://mlsanity.com/paper/1801.04134/) (2018)

## Built on by (most cited first, 11 of 11)

- [PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation Tasks](https://mlsanity.com/paper/2407.00278/) (2024)
- [Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models](https://mlsanity.com/paper/2309.04316/) (2023)
- [NativeMEM: Native Memory Compression for Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2607.06678/) (2026)
- [Robots Can Multitask Too: Integrating a Memory Architecture and LLMs for Enhanced Cross-Task Robot Action Generation](https://mlsanity.com/paper/2407.13505/) (2024)
- [Episodic Memory Verbalization using Hierarchical Representations of Life-Long Robot Experience](https://mlsanity.com/paper/2409.17702/) (2024)
- [How Metacognitive Architectures Remember Their Own Thoughts: A Systematic Review](https://mlsanity.com/paper/2503.13467/) (2025)
- [eMEM: A Hybrid Spatio-Temporal Memory System For Embodied Agents](https://mlsanity.com/paper/2606.03374/) (2026)
- [Towards a cognitive architecture to enable natural language interaction in co-constructive task learning](https://mlsanity.com/paper/2503.23760/) (2025)
- [Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment](https://mlsanity.com/paper/2604.11306/) (2026)
- [Human-Inspired Context-Selective Multimodal Memory for Social Robots](https://mlsanity.com/paper/2604.12081/) (2026)
- [Not Forgotten: Implementation and Evaluation of a Personalized Episodic Memory for the Humanoid Robot Head Kim](https://mlsanity.com/paper/2607.24190/) (2026)

## Abstract (arXiv)

Cognitive agents such as humans and robots perceive their environment through an abundance of sensors producing streams of data that need to be processed to generate intelligent behavior. A key question of cognition-enabled and AI-driven robotics is how to organize and manage knowledge efficiently in a cognitive robot control architecture. We argue, that memory is a central active component of such architectures that mediates between semantic and sensorimotor representations, orchestrates the flow of data streams and events between different processes and provides the components of a cognitive architecture with data-driven services for the abstraction of semantics from sensorimotor data, the parametrization of symbolic plans for execution and prediction of action effects. Based on related work, and the experience gained in developing our ARMAR humanoid robot systems, we identified conceptual and technical requirements of a memory system as central component of cognitive robot control architecture that facilitate the realization of high-level cognitive abilities such as explaining, reasoning, prospection, simulation and augmentation. Conceptually, a memory should be active, support multi-modal data representations, associate knowledge, be introspective, and have an inherently episodic structure. Technically, the memory should support a distributed design, be access-efficient and capable of long-term data storage. We introduce the memory system for our cognitive robot control architecture and its implementation in the robot software framework ArmarX. We evaluate the efficiency of the memory system with respect to transfer speeds, compression, reproduction and prediction capabilities.