license: cc-by-sa-4.0
task_categories:
- reinforcement-learning
- robotics
language:
- en
annotations_creators:
- experts-generated
tags:
- self-driving
- robotics navigation
pretty_name: FrodoBots 2K Dataset
Dataset Description
- Homepage: https://www.frodobots.ai/
- Hours of tele-operation: ~2,000 Hrs
- Dataset Size: 700+ GB
- Point of Contact: michael.cho@frodobots.com
FrodoBots 2K Dataset
The FrodoBots 2K Dataset is a diverse collection of camera footage, GPS, IMU, audio recordings & human control data collected from ~2,000 hours of tele-operated sidewalk robots driving in 10+ cities.
This dataset is collected from Earth Rovers, a global scavenger hunt "Drive to Earn" game developed by FrodoBots Lab.
Please join our Discord for discussions with fellow researchers/makers!
If you're interested in contributing driving data, you can buy your own unit(s) from our online shop (US$299 per unit) and start driving around your neighborhood (& earn in-game points in the process)!
If you're interested in testing out your AI models on our existing fleet of Earth Rovers in various cities or your own Earth Rover, feel free to DM Michael Cho on Twitter/X to gain access to our Remote Access SDK.
If you're interested in playing the game (ie. remotely driving an Earth Rover), you may join as a gamer at Earth Rovers School.
Dataset Summary
There are 7 types of data that are associated with a typical Earth Rovers drive, as follows:
Control data: Gamer's control inputs captured at a frequency of 10Hz (Ideal) as well as the RPM (revolutions per minute) readings for each of the 4 wheels on the robot.
GPS data: Latitude, longitude, and timestamp info collected during the robot drives at a frequency of 1Hz.
IMU (Inertial Measurement Unit) data: 9-DOF sensor data, including acceleration (captured at 100Hz), gyroscope (captured at 1Hz), and magnetometer info (captured at 1Hz), along with timestamp data.
Rear camera video: Video footage captured by the robot's rear-facing camera at a typical frame rate of 20 FPS with a resolution of 540x360.
Front camera video: Video footage captured by the robot's front-facing camera at a typical frame rate of 20 FPS with a resolution of 1024x576.
Microphone: Audio recordings captured by the robot's microphone, with a sample rate of 16000Hz, channel 1.
Speaker: Audio recordings of the robot's speaker output (ie. gamer's microphone), also with a sample rate of 16000Hz, channel 1.
Note: As of 12 May 2024, ~1,300 hrs are ready for download. The remaining ~700 hours are still undergoing data cleaning and will be available for download by end May or early June.
Loom Video Explanation
In total, there were 9,000+ individual driving sessions recorded. The chart below shows the distribution of individual driving session duration.
These drives were done with Earth Rovers in 10+ cities. The chart below shows the distribution of recorded driving duration in the various cities.
About FrodoBots
FrodoBots is an open-world video driving game where gamers remotely control sidewalk robots to complete missions in different cities (here's a video about the robot).
The game objective is to complete the pre-defined navigation missions in single-player mode (see video) or gather digital items in the multiplayer arena mode (see video).
Motivations for open-sourcing the dataset
The team behind FrodoBots is focused on building an open-world video gaming experience using real-life robots (we call it "robotic gaming"). A by-product of gamers playing the game in real-life is the accompanying dataset that's generated.
By sharing this dataset with the research community, we hope to see new innovations that can be tested (via our SDK) directly on our fleet of FrodoBots, and then ultimately deployed back into our production environment in order to create a more fun and safer game.
Download
Download FrodoBots dataset using the link in this csv file.
Helper code
We've provided a helpercode.ipynb file that will hopefully serve as a quick-start for researchers to play around with the dataset.
Contributions
The team at FrodoBots Lab created this dataset, including Michael Cho, Sam Cho, Aaron Tung, Niresh Dravin & Santiago Pravisani.