Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots
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Abstract (arXiv)
In this paper, we present a model for semantic memory that allows machines to collect information and experiences to become more proficient with time. Post semantic analysis of the sensory and other related data, the processed information is stored in the knowledge graph which is then used to comprehend the work instructions expressed in natural language. This imparts industrial robots cognitive behavior to execute the required tasks in a deterministic manner. The paper outlines the architecture of the system along with an implementation of the proposal.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 46 references that are in the map. First posted on arXiv 4 Jan 2021.
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5 of 46 references.
References · 5
- 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
- 2017Deep Learning in Robotics: A Review of Recent Researchcited by 42
- 2018YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computerscited by 34
- 2020Self-supervised Learning for Precise Pick-and-place without Object Modelcited by 20
- 2017Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learningcited by 15
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