Awesome

Emergence of exploratory look-around behaviors through active observation completion

This repository contains the code for the paper:

Emergence of exploratory look-around behaviors through active observation completion
Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman
Science Robotics 2019

Note

This is a cleaned version of the original code used to generate the results from the paper. As a result, there may be small differences in the actual results obtained by training models using the code. Please contact me if further details are needed.

Setup

• First install anaconda and setup a new environment. Install anaconda from: https://www.anaconda.com/download/

conda create -n spl python=2.7
source activate spl

• Clone this repository and setup requirements through pip.

git clone https://github.com/srama2512/visual-exploration.git
cd visual-exploration
pip install -r requirements.txt

• Download preprocessed SUN360 and ModelNet data.

mkdir data
cd data
wget http://vision.cs.utexas.edu/projects/sidekicks/scirobo-2019-data.zip
unzip scirobo-2019-data.zip

• Add the repository to PYTHONPATH. Please add this line to ~/.bashrc.

export PYTHONPATH=<path-to-repository>:$PYTHONPATH

The downloaded zip file will consist of the following data:

• Lookaround task:
  • data/SUN360/lookaround.h5
  • data/ModelNet/lookaround_modelnet40.h5
  • data/ModelNet/lookaround_modelnet10.h5
• Recognition task:
  • data/SUN360/recognition.h5
  • data/ModelNet/recognition_modelnet10.h5
• Light source localization task:
  • data/ModelNet/lsl_modelnet10.h5
  • data/ModelNet/lsl_labels_modelnet10.h5
• Metric tasks:
  • data/ModelNet/metric_labels_modelnet10.h5

The source code for individual tasks are provided in src/. Each task has its own train.py and eval.py scripts.

TODO

• Provide pre-trained models
• Instructions for evaluating pre-trained models
• Instructions for training task-specific models