TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

Related tags

Deep Learningtacto
Overview

TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors

License: MIT PyPI CircleCI Code style: black DIGIT-logo

TACTO Simulator

This package provides a simulator for vision-based tactile sensors, such as DIGIT. It provides models for the integration with PyBullet, as well as a renderer of touch readings.

NOTE: the simulator is not meant to provide a physically accurate dynamics of the contacts (e.g., deformation, friction), but rather relies on existing physics engines.

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

Installation

The preferred way of installation is through PyPi:

pip install tacto

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

git clone https://github.com/facebookresearch/tacto.git
cd tacto
pip install -e .

Content

This package contain several components:

  1. A renderer to simulate readings from vision-based tactile sensors.
  2. An API to simulate vision-based tactile sensors in PyBullet.
  3. Mesh models and configuration files for the DIGIT and Omnitact sensors.

Usage

Additional packages (torch, gym, pybulletX) are required to run the following examples. You can install them by pip install -r requirements/examples.txt.

For a basic example on how to use TACTO in conjunction with PyBullet look at [TBD],

For an example of how to use just the renderer engine look at examples/demo_render.py.

For advanced examples of how to use the simulator with PyBullet look at the examples folder.

Demo DIGIT

Demo Allegro

Demo OmniTact

Demo Grasp

Demo Rolling

NOTE: the renderer requires a screen. For rendering headless, use the "EGL" mode with GPU and CUDA driver or "OSMESA" with CPU. See PyRender for more details.

License

This project is licensed under MIT license, as found in the LICENSE file.

Citing

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

@Article{Wang2020TACTO,
  author  = {Wang, Shaoxiong and Lambeta, Mike and Chou, Lambeta and Calandra, Roberto},
  title   = {TACTO: A Fast, Flexible and Open-source Simulator for High-Resolution Vision-based Tactile Sensors},
  journal = {Arxiv},
  year    = {2020},
  url     = {https://arxiv.org/abs/2012.08456},
}
Owner
Facebook Research
Facebook Research
Tutorials, assignments, and competitions for MIT Deep Learning related courses.

MIT Deep Learning This repository is a collection of tutorials for MIT Deep Learning courses. More added as courses progress. Tutorial: Deep Learning

Lex Fridman 9.5k Jan 07, 2023
Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

This project is now archived. It's been fun working on it, but it's time for me to move on. Thank you for all the support and feedback over the last c

Max Pumperla 2.1k Jan 03, 2023
Reinforcement Learning Theory Book (rus)

Reinforcement Learning Theory Book (rus)

qbrick 206 Nov 27, 2022
A general-purpose programming language, focused on simplicity, safety and stability.

The Rivet programming language A general-purpose programming language, focused on simplicity, safety and stability. Rivet's goal is to be a very power

The Rivet programming language 17 Dec 29, 2022
Official PyTorch Implementation of Learning Architectures for Binary Networks

Learning Architectures for Binary Networks An Pytorch Implementation of the paper Learning Architectures for Binary Networks (BNAS) (ECCV 2020) If you

Computer Vision Lab. @ GIST 25 Jun 09, 2022
Simple implementation of Mobile-Former on Pytorch

Simple-implementation-of-Mobile-Former At present, only the model but no trained. There may be some bug in the code, and some details may be different

Acheung 103 Dec 31, 2022
Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation

Implicit Internal Video Inpainting Implementation for our ICCV2021 paper: Internal Video Inpainting by Implicit Long-range Propagation paper | project

202 Dec 30, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch The official pytorch implementation of the paper "Towards Faster and Stabilize

Bingchen Liu 455 Jan 08, 2023
Apply AnimeGAN-v2 across frames of a video clip

title emoji colorFrom colorTo sdk app_file pinned AnimeGAN-v2 For Videos 🔥 blue red gradio app.py false AnimeGAN-v2 For Videos Apply AnimeGAN-v2 acro

Nathan Raw 36 Oct 18, 2022
SMIS - Semantically Multi-modal Image Synthesis(CVPR 2020)

Semantically Multi-modal Image Synthesis Project page / Paper / Demo Semantically Multi-modal Image Synthesis(CVPR2020). Zhen Zhu, Zhiliang Xu, Anshen

316 Dec 01, 2022
Multi-Task Deep Neural Networks for Natural Language Understanding

New Release We released Adversarial training for both LM pre-training/finetuning and f-divergence. Large-scale Adversarial training for LMs: ALUM code

Xiaodong 2.1k Dec 30, 2022
Federated_learning codes used for the the paper "Evaluation of Federated Learning Aggregation Algorithms" and "A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison"

Federated Distance (FedDist) This is the code accompanying the Percom2021 paper "A Federated Learning Aggregation Algorithm for Pervasive Computing: E

GETALP 8 Jan 03, 2023
Embeddinghub is a database built for machine learning embeddings.

Embeddinghub is a database built for machine learning embeddings.

Featureform 1.2k Jan 01, 2023
Geometry-Free View Synthesis: Transformers and no 3D Priors

Geometry-Free View Synthesis: Transformers and no 3D Priors Geometry-Free View Synthesis: Transformers and no 3D Priors Robin Rombach*, Patrick Esser*

CompVis Heidelberg 293 Dec 22, 2022
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification (NeurIPS 2021)

Graph Posterior Network This is the official code repository to the paper Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classifica

Maximilian Stadler 30 Dec 05, 2022
PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation

StructDepth PyTorch implementation of our ICCV2021 paper: StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimat

SJTU-ViSYS 112 Nov 28, 2022
Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target i

NanYoMy 13 Oct 09, 2022
Serverless proxy for Spark cluster

Hydrosphere Mist Hydrosphere Mist is a serverless proxy for Spark cluster. Mist provides a new functional programming framework and deployment model f

hydrosphere.io 317 Dec 01, 2022