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.

翻译：认知主体（如人类和机器人）通过大量传感器感知环境，产生需要处理的数据流以生成智能行为。认知赋能与人工智能驱动的机器人学中的一个关键问题是：如何在认知机器人控制架构中高效地组织和管理知识。我们认为，记忆是该类架构中的核心主动组件，它在语义表征与感觉运动表征之间进行中介，协调不同进程之间的数据流与事件流，并为认知架构的组件提供数据驱动的服务，以实现从感觉运动数据中抽象语义、参数化符号化计划用于执行以及预测动作效果。基于相关研究以及开发ARMAR人形机器人系统过程中积累的经验，我们识别出记忆系统作为认知机器人控制架构核心组件的概念与技术需求，这些需求有助于实现解释、推理、预见、模拟与增强等高级认知能力。在概念层面，记忆应具备主动性、支持多模态数据表征、关联知识、具备内省能力，并具有内在的片段化结构。在技术层面，记忆应支持分布式设计、具备高效访问能力以及长期数据存储能力。我们介绍了用于认知机器人控制架构的记忆系统及其在机器人软件框架ArmarX中的实现。我们评估了该记忆系统在传输速度、压缩、复现与预测能力方面的效率。