Python interface for the DIGIT tactile sensor

Overview

DIGIT-INTERFACE

License: CC BY-NC 4.0 PyPI DIGIT-logo

Python interface for the DIGIT tactile sensor.

For updates and discussions please join the #DIGIT channel at the www.touch-sensing.org community.

Installation

The preferred way of installation is through PyPi:

pip install digit-interface

Alternatively, you can manually clone the repository and install the package using:

git clone https://github.com/facebookresearch/digit-interface.git 
cd digit-interface
pip install -r requirements.txt
python setup.py install

If you cannot access the device by serial number on your system follow adding DIGIT udev Rule

Usage

The default connection method to the DIGIT tactile sensor is through the unique device serial number. The serial number is found on the back of each DIGIT. See List all connected DIGIT's to find device serial numbers which are connected to the host.

Once you have the device serial number, reading data from the sensor should be as easy as

from digit_interface.digit import Digit
 
d = Digit("D12345") # Unique serial number
d.connect()
d.show_view()
d.disconnect()

Upon connection each DIGIT device initializes with a default stream resolution of VGA: 640x480 at 30fps

Further Usage

List all connected DIGIT's:

To list all connected DIGIT's and display sensor information:

from digit_interface.digit_handler import DigitHandler

digits = DigitHandler.list_digits()
Obtain a single frame:
from digit_interface.digit import Digit

d = Digit("D12345") # Unique serial number
d.connect()
frame = d.get_frame()
List supported stream formats:

Additional streams are supported, these streams vary in resolution and frames per second.

To list the available stream formats:

from digit_interface.digit_handler import DigitHandler

print("Supported streams: \n {}".format(DigitHandler.STREAMS))
Change resolution:
d.set_resolution(DigitHandler.STREAMS["QVGA"])
Change FPS,

Based on supported fps for each respective resolution. All streams support pre-defined resolutions which can be found in DigitHandler.STREAMS

d.set_fps(DigitHandler.STREAMS["QVGA"]["fps"]["15fps"])

Adding DIGIT udev Rule

Add your user to the plugdev group,

adduser username plugdev

Copy udev rule,

sudo cp ./udev/50-DIGIT.rules /lib/udev/rules.d/

Reload rules,

sudo udevadm control --reload
sudo udevadm trigger

Replug the DIGIT device into host.

License

This code is licensed under CC-by-NC, as found in the LICENSE file.

Citing

If you use this project in your research, please cite this paper:

@Article{Lambeta2020DIGIT,
  author  = {Lambeta, Mike and Chou, Po-Wei and Tian, Stephen and Yang, Brian and Maloon, Benjamin and Victoria Rose Most and Stroud, Dave and Santos, Raymond and Byagowi, Ahmad and Kammerer, Gregg and Jayaraman, Dinesh and Calandra, Roberto},
  title   = {{DIGIT}: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  year    = {2020},
  volume  = {5},
  number  = {3},
  pages   = {3838--3845},
  doi     = {10.1109/LRA.2020.2977257},
}
Owner
Facebook Research
Facebook Research
Exploring Visual Engagement Signals for Representation Learning

Exploring Visual Engagement Signals for Representation Learning Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie and Ser-Nam Lim C

Menglin Jia 9 Jul 23, 2022
ICSS - Interactive Continual Semantic Segmentation

Presentation This repository contains the code of our paper: Weakly-supervised c

Alteia 9 Jul 23, 2022
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)

PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision Kehong Gong*, Bingbing Li*, Jianfeng Zhang*, Ta

256 Dec 28, 2022
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

HierarchicyBandit Introduction This is the implementation of WSDM 2022 paper : Show Me the Whole World: Towards Entire Item Space Exploration for Inte

yu song 5 Sep 09, 2022
Run Effective Large Batch Contrastive Learning on Limited Memory GPU

Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr

Luyu Gao 198 Dec 29, 2022
Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi.

Spchcat Speech recognition tool to convert audio to text transcripts, for Linux and Raspberry Pi. Description spchcat is a command-line tool that read

Pete Warden 279 Jan 03, 2023
A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

A computational optimization project towards the goal of gerrymandering the results of a hypothetical election in the UK.

Emma 1 Jan 18, 2022
Breast Cancer Detection 🔬 ITI "AI_Pro" Graduation Project

BreastCancerDetection - This program is designed to predict two severity of abnormalities associated with breast cancer cells: benign and malignant. Mammograms from MIAS is preprocessed and features

6 Nov 29, 2022
Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE

SMU A Tensorflow Implementation of SMU: SMOOTH ACTIVATION FUNCTION FOR DEEP NETWORKS USING SMOOTHING MAXIMUM TECHNIQUE arXiv https://arxiv.org/abs/211

Fuhang 5 Jan 18, 2022
Official implementation of our paper "Learning to Bootstrap for Combating Label Noise"

Learning to Bootstrap for Combating Label Noise This repo is the official implementation of our paper "Learning to Bootstrap for Combating Label Noise

21 Apr 09, 2022
REBEL: Relation Extraction By End-to-end Language generation

REBEL: Relation Extraction By End-to-end Language generation This is the repository for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By

Babelscape 222 Jan 06, 2023
Toolbox of models, callbacks, and datasets for AI/ML researchers.

Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch Website • Installation • Main

Pytorch Lightning 1.4k Dec 30, 2022
This repository accompanies the ACM TOIS paper "What can I cook with these ingredients?" - Understanding cooking-related information needs in conversational search

In this repository you find data that has been gathered when conducting in-situ experiments in a conversational cooking setting. These data include tr

6 Sep 22, 2022
A Python package for faster, safer, and simpler ML processes

Bender 🤖 A Python package for faster, safer, and simpler ML processes. Why use bender? Bender will make your machine learning processes, faster, safe

Otovo 6 Dec 13, 2022
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
Network Pruning That Matters: A Case Study on Retraining Variants (ICLR 2021)

Network Pruning That Matters: A Case Study on Retraining Variants (ICLR 2021)

Duong H. Le 18 Jun 13, 2022
FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

Gaurav Parmar 606 Jan 06, 2023
This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

MPMAB_BEACON This is code used for the paper "Decentralized Multi-player Multi-armed Bandits: Beyond Linear Reward Functions", Neurips 2021. Requireme

Cong Shen Research Group 0 Oct 26, 2021
Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

DPGNN This repository is an official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Im

Yu Wang (Jack) 18 Oct 12, 2022
mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms.

mbrl-lib is a toolbox for facilitating development of Model-Based Reinforcement Learning algorithms. It provides easily interchangeable modeling and planning components, and a set of utility function

Facebook Research 724 Jan 04, 2023