[2024.10.12] We release the full paper on arXiv: link.
Embodied intelligence is considered one of the most promising directions for artificial general intelligence, with human-like abilities interacting with the world. However, most works focus on bounded indoor environments, with limited literature on open-world scenarios. To address this, we release a new benchmark platform, named Embodied City, for embodied intelligence in urban environments. This platform includes a simulator and datasets on representative tasks for embodied intelligence in an urban environment. You can either request the API key to access the online deployed environment, or download it to deploy in your own server.
We construct an environment where the agents🤖 can perceive, reason, and take actions. The basic environment of the simulator includes a large business district in Beijing, one of the biggest city in China, in which we build 3D model for buildings, streets, and other elements, hosted by Unreal Engine.
We first manually use Blender4 to create the 3D asserts of the buildings, for which we use the streetview services of Baidu Map and Amap. The city level detail includes a variety of building types such as office towers🏢, shopping malls🏬, residential complexes🏠, and public facilities🏫. These models are textured and detailed to closely resemble their real-world counterparts to enhance realism in the simulation.
The streets are modeled to include all necessary components such as lanes🛣️, intersections❌, traffic signals🚦, and road markings⬆️. We also incorporate pedestrian pathways, cycling lanes, and parking areas. Data from traffic monitoring systems and mapping services help ensure that the street layout and traffic flow patterns are accurate and realistic.
Other elements include street furniture🚸 (benches, streetlights, signs) , vegetation🌳 (trees, shrubs, lawns), and urban amenities🚉 (bus stops, metro-entrances, public restrooms). These are also created using Blender, based on real-world references from the street view services mentioned above. Additionally, dynamic elements like vehicles🚗 and pedestrians🚶 are simulated to move realistically within the environment, contributing to the liveliness and accuracy of the urban simulation. The simulation algorithms of vehicles and pedestrians are based on Mirage Simulation System.
We provide a simple Python SDK to interact with the Embodied City API. Users can easily achieve perception and control of drone agents through the following functions. When the command is issued via the API, changes in the agent's first-person view will be observed in the Console.
Before using the SDK, you need to acquire a agent and obtain its token signing up at Embodied City. In the website, you should go to the Console page, choose an available drone, and click on the Acquire button.
❗️The token is a secret key that should not be shared with anyone.
❗️The token will expire after a certain period of time if you do not use it.
pip install embodiedcityfrom embodiedcity import DroneClient, ImageType, CameraID
base_url = "https://embodied-ai.city"
drone_id = "xxx"
token = "xxxxxxxx"
client = DroneClient(base_url, drone_id, token)# Move the drone forward by 10 meter (Short movement distance may result in action failure)
client.move_back_forth(10)# Get a RGB image from the front-center camera
image = client.get_image(ImageType.Scene, CameraID.FrontCenter)# Get an image of the depth from the front-center camera
image = client.get_image(ImageType.DepthPlanar, CameraID.FrontCenter)Here is the detailed API documentation.
After you finish using the drone, you should release it to make it available for others. You can do this by clicking on the Release button in the Console page.
It is possible that the drone collided with a building. Try issuing a command to move the drone in a direction without obstacles. Alternatively, use the function DroneClient.move_to_position to force it to a specified location.
Please download and install the full embodiedcity simulator.
In the Embodied City, we define five key embodied tasks that reflect three essential human-like abilities for intelligent agents in an open world: perception, reasoning, and decision-making. For perception, we focus on the task of embodied first-view scene understanding; for reasoning, we address embodied question answering and dialogue; and for decision-making, we include embodied action (visual-language navigation) and embodied task planning.
Download and extract the full embodiedcity simulator of offline version. Users can download the offline simulation environment for local deployment to train and test agents. The platform provides versions compatible with Windows, Linux, macOS, and other operating systems, enabling quick deployment and testing. For download links, please contact: [email protected]
conda env create -n EmbodiedCity -f environment.yml
conda activate EmbodiedCityor
conda create -n EmbodiedCity python=3.10
conda activate EmbodiedCity
pip install -r requirements.txtWe provides an example of a Vision-Language Navigation (VLN) task implementation.
- Code: The main code for the VLN task is located in embodied_vln.py.
- Dataset: The corresponding dataset files are located in:
- Datasets/vln/start_loc.txt - Defines the starting locations and instructions for each VLN task sample.
- Datasets/vln/label - Contains the ground truth trajectories.
In embodied_vln.py, the VLN_evaluator class is defined. You need to provide the dataset path, the model to be evaluated, and the corresponding API key.
model = "xxxxx" # LM models, e.g., "claude-3-haiku-20240307", "gpt-4o"
api_key = "xxxxxxxxx" # Fill in your API keyvln_eval = VLN_evaluator("dataset/vln", model, api_key)vln_eval.evaluation()We support multimodal models from OpenAI and Claude. If you wish to use a custom model, you can modify the LM_VLN class in utils.py.
The evaluation process will activate the simulator's drone, running through each VLN task sample. The performance of the model will be quantified using the following three metrics:
- Success Rate (SR) measures the proportion of navigation episodes where the agent successfully reaches the target location within a specified margin of error
- SPL (Success weighted by Path Length) is a metric that considers both the success rate and the efficiency of the path taken by the agent. It accounts for how closely the agent's path length matches the optimal path length.
- Navigation Error (NE) measures the average distance from the agent's final location to the target destination.
If you would like to perform the tasks of embodied first-view scene understanding, question answering, dialogue, and task planning, please see the examples in embodied_tasks.py. Corresponding dataset is in Datasets/Imgs and Datasets/Imgs_label.
In the embodied_tasks.py file, you should first define the queries you want to evaluate and place them into the query_dict dictionary. Here are examples for different tasks:
- Embodied First-view Scene Understanding:
query_dict['scene'] - Embodied Question Answering:
query_dict['qa1'],query_dict['qa2'],query_dict['qa3'] - Embodied Dialogue:
query_dict['dialog1'],query_dict['dialog2'],query_dict['dialog3'] - Embodied Task Planning:
query_dict['tp1'],query_dict['tp2']
# You need to specify the model and the corresponding API key. Supported models include multimodal models from OpenAI, Claude, and Qwen.
model = "xxxxx" # LM models, e.g., "claude-3-haiku-20240307", "gpt-4o"
api_key = "xxxxxxxxx" # Fill in API keyembodied_tasks = EmbodiedTasks(query_dict, model, api_key)# The results of the model's performance will be saved in the `./results/` folder, named as `task_type_model_name.csv`.
embodied_tasks.run()# You can evaluate the model's performance using
embodied_tasks.evaluate()This will provide quantitative metrics including BLEU-1, BLEU-2, BLEU-3, BLEU-4, ROUGE, METEOR, and CIDEr.
Besides, RGB observations are stored offline in the ./Datasets/Imgs folder for convenience. Labels for different tasks are stored in ./Datasets/Imgs_label.
The first-view scene understanding requires the agent able to well observe the environment, and give the accurate description, which could considered as a basic ability for the further tasks. We observe from different perspectives at the same location, generating a set of RGB images as the input of scene understanding. The output is the textual description for the given scene images.
With the first-view observation, the embodied agent could be further fed with a query posed in natural language about the environment. The input includes both the first-view RGB images and a query about the environment. The output should be the direct textual responses to the question. Here we provide three questions:
- How many traffic lights can be observed around in total?
- Is there a building on the left side? What color is it?
- Are you facing the road, the building, or the greenery?
Embodied dialogue involves ongoing interactions where the agent engages in a back-and-forth conversation with the user。 This requires maintaining context and understanding the flow of dialogue. Therefore, the input includes embodied observations and multi-round queries, and the output is the multi-round responses. Here we provide three dialogues:
- May I ask if there are any prominent waypoints around? -> Where are they located respectively?
- May I ask what color the building on the left is? -> Where is it located relative to the road ahead?
- How many trees are there in the rear view? -> What colors are they respectively?
Embodied Action, often referred to as Vision-and-Language Navigation (VLN), is a research area in artificial intelligence that focuses on enabling an agent to navigate an environment based on natural language instructions. The input combines visual perception and natural language instructions to guide the agent through complex environments. The output is the action sequences following the language instructions.
The decision-making in the real world does not have explicit instructions; otherwise, there is only a task goal. It is significant for the embodied agents to be able to compose the complex and long-term task goals into several sub-tasks, which we refer to as embodied task planning. The input is the first-view observations and a given natural language described task goal, and the output should be a series of sub-tasks that the agent plans to execute. Here we provide three tasks:
- I want to have a cup of coffee at ALL-Star coffee shop, but I have not brought any money. What should I do? Please give a chain-like plan.
- I need to get an emergency medicine from the pharmacy, but I do not know the way. What should I do? Please give a chain-like plan.
- I lost my wallet nearby, and now I need to find it. What should I do? Please give a chain-like plan.
Please cite our paper if you find EmbodiedCity helpful in your research.
@article{gao2024embodied,
title={EmbodiedCity: A Benchmark Platform for Embodied Agent in Real-world City Environment},
author={Gao, Chen and Zhao, Baining and Zhang, Weichen and Zhang, Jun and Mao, Jinzhu and Zheng, Zhiheng and Man, Fanhang and Fang, Jianjie and Zhou, Zile and Cui, Jinqiang and Chen, Xinlei and Li, Yong},
journal={arXiv preprint},
year={2024}
}