An educational tool to introduce AI planning concepts using mobile manipulator robots.

Overview

JEDAI Explains Decision-Making AI

Virtual Machine Image

The recommended way of using JEDAI is to use pre-configured Virtual Machine image that is available here: https://bit.ly/2WccU4K

To setup the system manually, you can use the steps given below:

Tutorial

A short video tutorial on how to use JEDAI is available here: https://bit.ly/3BmQugi

Running JEDAI

Use this command to start JEDAI from the JEDAI source directory (~/JEDAI/ in VM Image).

./start_jedai.sh

Alternatively execute this command:

python3 manage.py runserver

The output of this command includes a link to the development server hosting the frontend.

You can stop the execution anytime using this command from the JEDAI source directory (~/JEDAI/ in VM Image):

./stop_jedai.sh

Installing JEDAI on a new system

Requirements

  • Ubuntu 18.04
  • Python 2 and 3
  • Validate: https://github.com/KCL-Planning/VAL
    1. Retrieve and enter the repo:

      git clone https://github.com/KCL-Planning/VAL

      cd VAL

    2. Build the binary:

      ./scripts/linux/build_linux64.sh all Release

      • This will put Validate in <PARENT_DIR>/VAL/build/linux64/Release/bin

NOTE: JEDAI is tested extensively with Chromium (including Edge, Vivaldi, and Google Chrome). Support on other browsers is not guaranteed.

Setup

  1. Retrieve the TMP_Merged submodule by running the following in the project root (unless you already have TMP_Merged somewhere else on your system and want to use that, in which case you can try a symlink):

    git clone https://github.com/AAIR-lab/Anytime-Task-and-Motion-Policies.git TMP_Merged

    1. You must then install the dependencies for the submodule (this will probably take a while):

      bash TMP_Merged/install_tmp_dependencies.sh

    2. Also make sure to check out the correct branch of the submodule:

      cd TMP_Merged

      git checkout origin/TMP_JEDAI

  2. Install the web framework:

    pip3 install django

  3. Install the YAML library:

    pip3 install PyYAML

  4. Install the PDDL library:

    pip3 install pddlpy

    • If you get an error while running the code about a missing module named __builtin__ in the antlr4 library, then running this should help:

      pip3 install antlr4-python3-runtime==4.7

  5. Install the imaging library:

    pip3 install Pillow

  6. Check that PYTHON_2_PATH and VAL_PATH in config.py are pointing to the corresponding binaries on your system.

You are required to submit a domain and problem file, as well as a .dae environment file. See the test_domains directory for examples.

TMP submodule

After installing its dependencies, the TMP submodule should work out of the box, with environments popping up and giving a demonstration of successful plans. If you get any strange import errors from TMP despite packages seeming to be installed correctly, double-check your all your environment variables (especially if using an IDE like PyCharm).

Contributors

Trevor Angle
Naman Shah
Kiran Prasad
Pulkit Verma
Amruta Tapadiya
Kyle Atkinson
Chirav Dave
Judith Rosenke
Rushang Karia
Siddharth Srivastava

Owner
Autonomous Agents and Intelligent Robots
ASU research group focusing on well-founded and reliable assistive AI systems
Autonomous Agents and Intelligent Robots
A minimal implementation of face-detection models using flask, gunicorn, nginx, docker, and docker-compose

Face-Detection-flask-gunicorn-nginx-docker This is a simple implementation of dockerized face-detection restful-API implemented with flask, Nginx, and

Pooya-Mohammadi 30 Dec 17, 2022
A benchmark dataset for emulating atmospheric radiative transfer in weather and climate models with machine learning (NeurIPS 2021 Datasets and Benchmarks Track)

ClimART - A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models Official PyTorch Implementation Using deep le

21 Dec 31, 2022
The source code for Adaptive Kernel Graph Neural Network at AAAI2022

AKGNN The source code for Adaptive Kernel Graph Neural Network at AAAI2022. Please cite our paper if you think our work is helpful to you: @inproceedi

11 Nov 25, 2022
Project of 'TBEFN: A Two-branch Exposure-fusion Network for Low-light Image Enhancement '

TBEFN: A Two-branch Exposure-fusion Network for Low-light Image Enhancement Codes for TMM20 paper "TBEFN: A Two-branch Exposure-fusion Network for Low

KUN LU 31 Nov 06, 2022
Table-Extractor 表格抽取

(t)able-(ex)tractor 本项目旨在实现pdf表格抽取。 Models 版面分析模块(Yolo) 表格结构抽取(ResNet + Transformer) 文字识别模块(CRNN + CTC Loss) Acknowledgements TableMaster attention-i

2 Jan 15, 2022
[SIGGRAPH 2020] Attribute2Font: Creating Fonts You Want From Attributes

Attr2Font Introduction This is the official PyTorch implementation of the Attribute2Font: Creating Fonts You Want From Attributes. Paper: arXiv | Rese

Yue Gao 200 Dec 15, 2022
Vector Quantized Diffusion Model for Text-to-Image Synthesis

Vector Quantized Diffusion Model for Text-to-Image Synthesis Due to company policy, I have to set microsoft/VQ-Diffusion to private for now, so I prov

Shuyang Gu 294 Jan 05, 2023
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 05, 2023
[ICLR 2021] "CPT: Efficient Deep Neural Network Training via Cyclic Precision" by Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin

CPT: Efficient Deep Neural Network Training via Cyclic Precision Yonggan Fu, Han Guo, Meng Li, Xin Yang, Yining Ding, Vikas Chandra, Yingyan Lin Accep

26 Oct 25, 2022
Learning hierarchical attention for weakly-supervised chest X-ray abnormality localization and diagnosis

Hierarchical Attention Mining (HAM) for weakly-supervised abnormality localization This is the official PyTorch implementation for the HAM method. Pap

Xi Ouyang 22 Jan 02, 2023
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features"

EDM-subgenre-classifier This repository contains the code for "Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Fea

11 Dec 20, 2022
The implementation of "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer"

Shuffle Transformer The implementation of "Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformer" Introduction Very recently, window-

87 Nov 29, 2022
Official implementation of Unfolded Deep Kernel Estimation for Blind Image Super-resolution.

Unfolded Deep Kernel Estimation for Blind Image Super-resolution Hongyi Zheng, Hongwei Yong, Lei Zhang, "Unfolded Deep Kernel Estimation for Blind Ima

Z80 15 Dec 26, 2022
To prepare an image processing model to classify the type of disaster based on the image dataset

Disaster Classificiation using CNNs bunnysaini/Disaster-Classificiation Goal To prepare an image processing model to classify the type of disaster bas

Bunny Saini 1 Jan 24, 2022
For auto aligning, cropping, and scaling HR and LR images for training image based neural networks

ImgAlign For auto aligning, cropping, and scaling HR and LR images for training image based neural networks Usage Make sure OpenCV is installed, 'pip

15 Dec 04, 2022
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs

ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs This is the code of paper ConE: Cone Embeddings for Multi-Hop Reasoning over Knowl

MIRA Lab 33 Dec 07, 2022
Sequential model-based optimization with a `scipy.optimize` interface

Scikit-Optimize Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements

Scikit-Optimize 2.5k Jan 04, 2023
Its a Plant Leaf Disease Detection System based on Machine Learning.

My_Project_Code Its a Plant Leaf Disease Detection System based on Machine Learning. I have used Tomato Leaves Dataset from kaggle. This system detect

Sanskriti Sidola 3 Jun 15, 2022
Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training".

Mixup-Data-Dependency Code associated with the paper "Towards Understanding the Data Dependency of Mixup-style Training". Running Alternating Line Exp

Muthu Chidambaram 0 Nov 11, 2021