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Datasets:
frodobots
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FrodoBots-2K

Tasks:
Reinforcement Learning
Robotics
Languages:
English
Tags:
self-driving
robotics navigation
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License:
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metadata
license: cc-by-sa-4.0
dataset_info:
  features:
    - name: id
      dtype: int64
  dataset_size: 900000000
task_categories:
  - reinforcement-learning
  - robotics
language:
  - en
annotations_creators:
  - experts-generated
tags:
  - self-driving
  - robotics navigation
pretty_name: FrodoBots (1K)

Dataset Description

FrodoBots 1K Dataset

The FrodoBots 1K Dataset is a diverse collection of camera footage, GPS, IMU, audio recordings & control data collected from close to 1K hours of tele-operated sidewalk robot driving in 10+ cities.

If you're interested in testing out your models on your own FrodoBots, you may reserve unit(s) from our upcoming [Kickstarter] campaign (from US$699).

If you're interested in testing out your models on our existing fleet of FrodoBots (in various cities), you may reach out to Michael Cho for access to an SDK.

Left to right in Stanford.gif
2 FrodoBots being tested in Stanford campus

Special Shoutout: We want to express our sincere gratitude to the team at BlurIt for sponsoring our video redaction efforts (ie. blurring of faces & license plates).

What's 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]).

As gamers play the game, it generates a valuable and multifaceted robotics dataset, which we have chosen to open source for the benefit of researchers and enthusiasts alike.

The Frodobots Gaming Dataset is a diverse collection of IMU, GPS, control data, camera footage, and audio recordings collected from sidewalk robots.

Dataset Overview

  1. Control data: This includes information about the robot's control inputs captured at a frequency of 10Hz.

  2. GPS data: This includes latitude, longitude, and timestamp information for each data point collected during the robot drives at a frequency of 1Hz.

  3. IMU (Inertial Measurement Unit) data: This provides 9-degree of freedom sensor data, including acceleration (captured at 100Hz), gyroscope (captured at 1Hz), and compass ride information (captured at 1Hz), along with timestamp data.

  4. Rear camera video: This offers video footage captured by the robot's rear-facing camera at 20 frames per second (FPS) with a resolution of 800x600.

  5. Front camera video: This consists of video footage captured by the robot's front-facing camera at 20 FPS with a resolution of 480x480.

  6. Microphone: This includes audio recordings captured by the robot's microphone, with a sample rate of 48000Hz.

  7. Speaker: This incorporates audio recordings of the robot's speaker output, also with a sample rate of 48000Hz.

Usage

For users not familiar with the structure of the data or those requiring assistance in manipulating and using it, please refer to the helpercode.ipynb file provided. This notebook contains Python code that helps understand and work with the dataset more effectively.