metadata

license: mit

Foundation Tactile (FoTa) - a multi-sensor multi-task large dataset for tactile sensing

Paper Code Colab

Jialiang (Alan) Zhao, Yuxiang Ma, Lirui Wang, and Edward H. Adelson

MIT CSAIL

[Project Website]

Overview

FoTa was released with Transferable Tactile Transformers (T3) as a large dataset for tactile representation learning. It aggregates some of the largest open-source tactile datasets, and it is released in a unified WebDataset format.

Fota contains over 3 million tactile images collected from 13 camera-based tactile sensors and 11 tasks.

File structure

After downloading and unzipping, the file structure of FoTa looks like:

dataset_1
    |---- train
        |---- count.txt
        |---- data_000000.tar
        |---- data_000001.tar
        |---- ...
    |---- val
        |---- count.txt
        |---- data_000000.tar
        |---- ...
dataset_2
:
dataset_n

Each .tar file is one sharded dataset. At runtime, wds (WebDataset) api automatically loads, shuffles, and unpacks all shards on demand. The nicest part of having a .tar file, instead of saving all raw data into matrices (e.g. .npz for zarr), is that .tar is easy to visualize without the need of any code. Simply double click on any .tar file to check its content.

Although you will never need to unpack a .tar manually (wds does that automatically), it helps to understand the logic and file structure.

data_000000.tar
    |---- file_name_1.jpg
    |---- file_name_1.json
    :
    |---- file_name_n.jpg
    |---- file_name_n.json

The .jpg files are tactile images, and the .json files store task-specific labels.

For more details on operations of the paper, checkout our GitHub repository and Colab tutorial.

Getting started

Checkout our Colab for a step-by-step tutorial!

Citation

MIT License.