# Embodied AI-Driven Operation of Smart Cities: A Concise Review arXiv: https://arxiv.org/abs/2108.09823 ยท HTML: https://mlsanity.com/paper/2108.09823/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 93 references that are in the map. - First posted on arXiv 22 Aug 2021. ## Builds on (most cited first, 30 of 40) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018) - [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016) - [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016) - [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015) - [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019) - [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017) - [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019) - [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019) - [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018) - [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018) - [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016) - [Reinforcement Learning with Unsupervised Auxiliary Tasks](https://mlsanity.com/paper/1611.05397/) (2016) - [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016) - [Embodied Question Answering](https://mlsanity.com/paper/1711.11543/) (2017) - [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://mlsanity.com/paper/1808.05326/) (2018) - [CAD2RL: Real Single-Image Flight without a Single Real Image](https://mlsanity.com/paper/1611.04201/) (2016) - [Cognitive Mapping and Planning for Visual Navigation](https://mlsanity.com/paper/1702.03920/) (2017) - [IQA: Visual Question Answering in Interactive Environments](https://mlsanity.com/paper/1712.03316/) (2017) - [Learning to Separate Object Sounds by Watching Unlabeled Video](https://mlsanity.com/paper/1804.01665/) (2018) - [Building Generalizable Agents with a Realistic and Rich 3D Environment](https://mlsanity.com/paper/1801.02209/) (2018) - [Embodied Question Answering in Photorealistic Environments with Point Cloud Perception](https://mlsanity.com/paper/1904.03461/) (2019) - [Grounded Language Learning in a Simulated 3D World](https://mlsanity.com/paper/1706.06551/) (2017) - [MINOS: Multimodal Indoor Simulator for Navigation in Complex Environments](https://mlsanity.com/paper/1712.03931/) (2017) - [Neural Modular Control for Embodied Question Answering](https://mlsanity.com/paper/1810.11181/) (2018) - [Video Captioning with Transferred Semantic Attributes](https://mlsanity.com/paper/1611.07675/) (2016) - [CHALET: Cornell House Agent Learning Environment](https://mlsanity.com/paper/1801.07357/) (2018) ## Built on by (most cited first, 6 of 6) - [Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community](https://mlsanity.com/paper/2307.09751/) (2023) - [A call for embodied AI](https://mlsanity.com/paper/2402.03824/) (2024) - [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023) - [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022) - [The application of Evolutionary and Nature Inspired Algorithms in Data Science and Data Analytics](https://mlsanity.com/paper/2202.03859/) (2022) - [Heterogeneous computing platform for real-time robotics](https://mlsanity.com/paper/2601.09755/) (2026) ## Abstract (arXiv) A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient. Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays. In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.