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
Generic template to bootstrap your PyTorch project with PyTorch Lightning, Hydra, W&B, and DVC.

NN Template Generic template to bootstrap your PyTorch project. Click on Use this Template and avoid writing boilerplate code for: PyTorch Lightning,

Luca Moschella 520 Dec 30, 2022
PICARD - Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

This is the official implementation of the following paper: Torsten Scholak, Nathan Schucher, Dzmitry Bahdanau. PICARD - Parsing Incrementally for Con

ElementAI 217 Jan 01, 2023
Deep Learning to Create StepMania SM FIles

StepCOVNet Running Audio to SM File Generator Currently only produces .txt files. Use SMDataTools to convert .txt to .sm python stepmania_note_generat

Chimezie Iwuanyanwu 8 Jan 08, 2023
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
This is the code for CVPR 2021 oral paper: Jigsaw Clustering for Unsupervised Visual Representation Learning

JigsawClustering Jigsaw Clustering for Unsupervised Visual Representation Learning Pengguang Chen, Shu Liu, Jiaya Jia Introduction This project provid

DV Lab 73 Sep 18, 2022
PyTorch implementation of DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images

DARDet PyTorch implementation of "DARDet: A Dense Anchor-free Rotated Object Detector in Aerial Images", [pdf]. Highlights: 1. We develop a new dense

41 Oct 23, 2022
N-Person-Check-Checker-Splitter - A calculator app use to divide checks

N-Person-Check-Checker-Splitter This is my from-scratch programmed calculator ap

2 Feb 15, 2022
Code repository for the paper Computer Vision User Entity Behavior Analytics

Computer Vision User Entity Behavior Analytics Code repository for "Computer Vision User Entity Behavior Analytics" Code Description dataset.csv As di

Sameer Khanna 2 Aug 20, 2022
Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.

Framework overview This library allows to quickly implement different architectures based on Reservoir Computing (the family of approaches popularized

Filippo Bianchi 249 Dec 21, 2022
[ACM MM 2021] TSA-Net: Tube Self-Attention Network for Action Quality Assessment

Tube Self-Attention Network (TSA-Net) This repository contains the PyTorch implementation for paper TSA-Net: Tube Self-Attention Network for Action Qu

ShunliWang 18 Dec 23, 2022
[ICCV'21] Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment

CKDN The official implementation of the ICCV2021 paper "Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment" O

Multimedia Research 50 Dec 13, 2022
shufflev2-yolov5:lighter, faster and easier to deploy

shufflev2-yolov5: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size

pogg 1.5k Jan 05, 2023
Code, final versions, and information on the Sparkfun Graphical Datasheets

Graphical Datasheets Code, final versions, and information on the SparkFun Graphical Datasheets. Generated Cells After Running Script Example Complete

SparkFun Electronics 102 Jan 05, 2023
CTRL-C: Camera calibration TRansformer with Line-Classification

CTRL-C: Camera calibration TRansformer with Line-Classification This repository contains the official code and pretrained models for CTRL-C (Camera ca

57 Nov 14, 2022
Using BERT+Bi-LSTM+CRF

Chinese Medical Entity Recognition Based on BERT+Bi-LSTM+CRF Step 1 I share the dataset on my google drive, please download the whole 'CCKS_2019_Task1

Xiang WU 55 Dec 21, 2022
Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

Overcooked-AI We suppose to apply traditional offline reinforcement learning technique to multi-agent algorithm. In this repository, we implemented be

Baek In-Chang 14 Sep 16, 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 of Kalman Filter in Python

Kalman Filter in Python This is a basic example of how Kalman filter works in Python. I do plan on refactoring and expanding this repo in the future.

Enoch Kan 35 Sep 11, 2022
Bytedance Inc. 2.5k Jan 06, 2023
A Python library for adversarial machine learning focusing on benchmarking adversarial robustness.

ARES This repository contains the code for ARES (Adversarial Robustness Evaluation for Safety), a Python library for adversarial machine learning rese

Tsinghua Machine Learning Group 377 Dec 20, 2022