Summary of Embodiedcity: a Benchmark Platform For Embodied Agent in Real-world City Environment, by Chen Gao et al.
EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment
by Chen Gao, Baining Zhao, Weichen Zhang, Jinzhu Mao, Jun Zhang, Zhiheng Zheng, Fanhang Man, Jianjie Fang, Zile Zhou, Jinqiang Cui, Xinlei Chen, Yong Li
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed benchmark platform enables evaluation of embodied artificial intelligence (AI) in real-world city environments. This platform simulates pedestrian and vehicle flows with high fidelity, using a combination of historically collected data and simulation algorithms. It provides input and output interfaces for access, allowing embodied agents to make decisions and obtain performance evaluations. The authors evaluate popular large language models for their embodied intelligence capabilities across different dimensions and difficulties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new platform for testing artificial intelligence that can work in real-world city environments. This is important because most AI research focuses on small, controlled spaces instead of the complexities of everyday life. The platform uses data and algorithms to simulate how people and vehicles move through cities. It also provides tools for AI systems to make decisions based on their surroundings. By testing popular AI models using this platform, researchers can see which ones are best at handling real-world challenges. |
