social humanoid robots with GPGPU and IoT

Overview

Social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT

Paper Authors

Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balakrishnan Prabhakaran, Yonas Tadesse

Initial design and development

UT Dallas senior design team

Sharon Choi, Manpreet Dhot, Mark Cordova, Luis Hall-Valdez, and Stephen Brooks

A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture

Currently, most social robots interact with their surroundings humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human-robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots.

Cite as:

DOI

https://doi.org/10.1016/j.robot.2020.103536

IEEE

M. Jafarzadeh, S. Brooks, S. Yu, B. Prabhakaran, and Y. Tadesse, “A wearable sensor vest for social humanoid robots with GPGPU, IOT, and Modular Software Architecture,” Robotics and Autonomous Systems, vol. 139, p. 103536, 2021.

MLA

Jafarzadeh, Mohsen, et al. "A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture." Robotics and Autonomous Systems 139 (2021): 103536.

APA

Jafarzadeh, M., Brooks, S., Yu, S., Prabhakaran, B., & Tadesse, Y. (2021). A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture. Robotics and Autonomous Systems, 139, 103536.

Chicago

Jafarzadeh, Mohsen, Stephen Brooks, Shimeng Yu, Balakrishnan Prabhakaran, and Yonas Tadesse. "A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture." Robotics and Autonomous Systems 139 (2021): 103536.

Harvard

Jafarzadeh, M., Brooks, S., Yu, S., Prabhakaran, B. and Tadesse, Y., 2021. A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture. Robotics and Autonomous Systems, 139, p.103536.

Vancouver

Jafarzadeh M, Brooks S, Yu S, Prabhakaran B, Tadesse Y. A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture. Robotics and Autonomous Systems. 2021 May 1;139:103536.

Bibtex

@article{Jafarzadeh2021robots,
title = {A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture},
journal = {Robotics and Autonomous Systems},
volume = {139},
pages = {103536},
year = {2021},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2020.103536},
url = {https://www.sciencedirect.com/science/article/pii/S0921889019306323},
author = {Mohsen Jafarzadeh and Stephen Brooks and Shimeng Yu and Balakrishnan Prabhakaran and Yonas Tadesse},
}

License

Copyright (c) 2020 Mohsen Jafarzadeh. All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
  3. All advertising materials mentioning features or use of this software must display the following acknowledgement: This product includes software developed by Mohsen Jafarzadeh, Stephen Brooks, Sharon Choi, Manpreet Dhot, Mark Cordova, Luis Hall-Valdez, and Shimeng Yu.
  4. Neither the name of the Mohsen Jafarzadeh nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY MOHSEN JAFARZADEH "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL MOHSEN JAFARZADEH BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Owner
http://www.mohsen-jafarzadeh.com
Program your own vulkan.gpuinfo.org query in Python. Used to determine baseline hardware for WebGPU.

query-gpuinfo-data License This software is not presently released under a license. The data in data/ is obtained under CC BY 4.0 as specified there.

Kai Ninomiya 5 Jul 18, 2022
PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021.

PAML PyTorch implementation of the paper: "Preference-Adaptive Meta-Learning for Cold-Start Recommendation", IJCAI, 2021. (Continuously updating ) Int

15 Nov 18, 2022
CVPR 2021: "The Spatially-Correlative Loss for Various Image Translation Tasks"

Spatially-Correlative Loss arXiv | website We provide the Pytorch implementation of "The Spatially-Correlative Loss for Various Image Translation Task

Chuanxia Zheng 89 Jan 04, 2023
A python library to build Model Trees with Linear Models at the leaves.

A python library to build Model Trees with Linear Models at the leaves.

Marco Cerliani 212 Dec 30, 2022
A simple log parser and summariser for IIS web server logs

IISLogFileParser A basic parser tool for IIS Logs which summarises findings from the log file. Inspired by the Gist https://gist.github.com/wh13371/e7

2 Mar 26, 2022
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection Introduction This repository includes codes and models of "Effect of De

Amir Abbasi 5 Sep 05, 2022
Extremely simple and fast extreme multi-class and multi-label classifiers.

napkinXC napkinXC is an extremely simple and fast library for extreme multi-class and multi-label classification, that focus of implementing various m

Marek Wydmuch 43 Nov 14, 2022
Little tool in python to watch anime from the terminal (the better way to watch anime)

ani-cli Script working again :), thanks to the fork by Dink4n for the alternative approach to by pass the captcha on gogoanime A cli to browse and wat

Harshith 4.5k Dec 31, 2022
Official release of MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer axriv: http://arxiv.org/abs/2112.13513

MSHT: Multi-stage Hybrid Transformer for the ROSE Image Analysis This is the official page of the MSHT with its experimental script and records. We de

Tianyi Zhang 53 Dec 27, 2022
Official Implementation of Domain-Aware Universal Style Transfer

Domain Aware Universal Style Transfer Official Pytorch Implementation of 'Domain Aware Universal Style Transfer' (ICCV 2021) Domain Aware Universal St

KibeomHong 80 Dec 30, 2022
VOneNet: CNNs with a Primary Visual Cortex Front-End

VOneNet: CNNs with a Primary Visual Cortex Front-End A family of biologically-inspired Convolutional Neural Networks (CNNs). VOneNets have the followi

The DiCarlo Lab at MIT 99 Dec 22, 2022
Pytorch implementation of our paper accepted by NeurIPS 2021 -- Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme

Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme (NeurIPS2021) (Link) Overview Prerequisites Linu

Shaojie Li 34 Mar 31, 2022
A general-purpose encoder-decoder framework for Tensorflow

READ THE DOCUMENTATION CONTRIBUTING A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summariz

Google 5.5k Jan 07, 2023
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

3 Nov 23, 2022
PyTorch implementation of "VRT: A Video Restoration Transformer"

VRT: A Video Restoration Transformer Jingyun Liang, Jiezhang Cao, Yuchen Fan, Kai Zhang, Rakesh Ranjan, Yawei Li, Radu Timofte, Luc Van Gool Computer

Jingyun Liang 837 Jan 09, 2023
(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

54 Aug 04, 2022
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.

Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi

18 Oct 20, 2022
BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalanced Tongue Data

Balanced-Evolutionary-Semi-Stacking Code for the paper ''BESS: Balanced Evolutionary Semi-Stacking for Disease Detection via Partially Labeled Imbalan

0 Jan 16, 2022
Keras community contributions

keras-contrib : Keras community contributions Keras-contrib is deprecated. Use TensorFlow Addons. The future of Keras-contrib: We're migrating to tens

Keras 1.6k Dec 21, 2022
Object Detection with YOLOv3

Object Detection with YOLOv3 Bu projede YOLOv3-608 modeli kullanılmıştır. Requirements Python 3.8 OpenCV Numpy Documentation Yolo ile ilgili detaylı b

Ayşe Konuş 0 Mar 27, 2022