A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX
| Autor: | Peller-Konrad, Fabian, Kartmann, Rainer, Dreher, Christian R. G., Meixner, Andre, Reister, Fabian, Grotz, Markus, Asfour, Tamim |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Robotics and Autonomous Systems (2023) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1016/j.robot.2023.104415 |
| Popis: | 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. Comment: 35 pages, 19 figures, submitted to RAS |
| Databáze: | arXiv |
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