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
Self-Regulated Learning for Egocentric Video Activity Anticipation

Self-Regulated Learning for Egocentric Video Activity Anticipation Introduction This is a Pytorch implementation of the model described in our paper:

qzhb 13 Sep 23, 2022
Python implementation of Project Fluent

Project Fluent This is a collection of Python packages to use the Fluent localization system. python-fluent consists of these packages: fluent.syntax

Project Fluent 155 Dec 28, 2022
PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series

Kaung 305 Jan 03, 2023
Real-time pose estimation accelerated with NVIDIA TensorRT

trt_pose Want to detect hand poses? Check out the new trt_pose_hand project for real-time hand pose and gesture recognition! trt_pose is aimed at enab

NVIDIA AI IOT 803 Jan 06, 2023
Sionna: An Open-Source Library for Next-Generation Physical Layer Research

Sionna: An Open-Source Library for Next-Generation Physical Layer Research Sionna™ is an open-source Python library for link-level simulations of digi

NVIDIA Research Projects 313 Dec 22, 2022
A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

Apurva R. Umredkar 2 Nov 25, 2022
[EMNLP 2020] Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Contextual Action Language Model (CALM) and the ClubFloyd Dataset Code and data for paper Keep CALM and Explore: Language Models for Action Generation

Princeton Natural Language Processing 43 Dec 16, 2022
Code Repository for The Kaggle Book, Published by Packt Publishing

The Kaggle Book Data analysis and machine learning for competitive data science Code Repository for The Kaggle Book, Published by Packt Publishing "Lu

Packt 1.6k Jan 07, 2023
Official code for MPG2: Multi-attribute Pizza Generator: Cross-domain Attribute Control with Conditional StyleGAN

This is the official code for Multi-attribute Pizza Generator (MPG2): Cross-domain Attribute Control with Conditional StyleGAN. Paper Demo Setup Envir

Fangda Han 5 Sep 01, 2022
A Unified Generative Framework for Various NER Subtasks.

This is the code for ACL-ICJNLP2021 paper A Unified Generative Framework for Various NER Subtasks. Install the package in the requirements.txt, then u

177 Jan 05, 2023
This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents".

Introduction This code is the implementation of the paper "Coherence-Based Distributed Document Representation Learning for Scientific Documents". If

tsc 0 Jan 11, 2022
Get the partition that a file belongs and the percentage of space that consumes

tinos_eisai_sy Get the partition that a file belongs and the percentage of space that consumes (works only with OSes that use the df command) tinos_ei

Konstantinos Patronas 6 Jan 24, 2022
PyTorch Implementation of Sparse DETR

Sparse DETR By Byungseok Roh*, Jaewoong Shin*, Wuhyun Shin*, and Saehoon Kim at Kakao Brain. (*: Equal contribution) This repository is an official im

Kakao Brain 113 Dec 28, 2022
Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch

Kim Seonghyeon 2.2k Jan 01, 2023
(JMLR'19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

Python Outlier Detection (PyOD) Deployment & Documentation & Stats Build Status & Coverage & Maintainability & License PyOD is a comprehensive and sca

Yue Zhao 6.6k Jan 03, 2023
Proposed n-stage Latent Dirichlet Allocation method - A Novel Approach for LDA

n-stage Latent Dirichlet Allocation (n-LDA) Proposed n-LDA & A Novel Approach for classical LDA Latent Dirichlet Allocation (LDA) is a generative prob

Anıl Güven 4 Mar 07, 2022
Use unsupervised and supervised learning to predict stocks

AIAlpha: Multilayer neural network architecture for stock return prediction This project is meant to be an advanced implementation of stacked neural n

Vivek Palaniappan 1.5k Dec 26, 2022
Pytorch codes for "Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation"

Self-Supervised-MVS This repository is the official PyTorch implementation of our AAAI 2021 paper: "Self-supervised Multi-view Stereo via Effective Co

hongbin_xu 127 Jan 04, 2023
PyTorch for Semantic Segmentation

PyTorch for Semantic Segmentation This repository contains some models for semantic segmentation and the pipeline of training and testing models, impl

Zijun Deng 1.7k Jan 06, 2023
Creating multimodal multitask models

Fusion Brain Challenge The English version of the document can be found here. Обновления 01.11 Мы выкладываем пример данных, аналогичных private test

Sber AI 43 Nov 28, 2022