# Dynamic Knowledge Graphs as Semantic Memory Model for Industrial Robots

arXiv: https://arxiv.org/abs/2101.01099 · HTML: https://mlsanity.com/paper/2101.01099/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 5 of 5)

- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Deep Learning in Robotics: A Review of Recent Research](https://mlsanity.com/paper/1707.07217/) (2017)
- [YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers](https://mlsanity.com/paper/1811.05588/) (2018)
- [Self-supervised Learning for Precise Pick-and-place without Object Model](https://mlsanity.com/paper/2006.08373/) (2020)
- [Dex-Net 3.0: Computing Robust Robot Vacuum Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning](https://mlsanity.com/paper/1709.06670/) (2017)

## 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.