Classification of Household Materials via Spectroscopy

Z. Erickson, N. Luskey, S. Chernova, and C. C. Kemp, "Classification of Household Materials via Spectroscopy", IEEE Robotics and Automation Letters (RA-L), 2019.

Project webpage: https://pwp.gatech.edu/hrl/smm50/

Download the SMM50 dataset

SMM50 dataset (16 MB): https://goo.gl/Xjh6x4
Dataset details can be found on the project webpage.

Use the following commands to download and extract the SMM50 dataset.

cd data
wget -O smm50.tar.gz https://goo.gl/Xjh6x4
tar -xvzf smm50.tar.gz
rm smm50.tar.gz

Running the code

Our models are implemented in Keras with the Tensorflow backend.
Results presented in table I from the paper can be computed using the following.

python learn.py -t 0

Generalization with leave-one-object-out validation results from figures 11 and 12 can be computed using the command below.

python learn.py -t 1

The generalization results with increasing numbers of objects can be recomputed using the command below. This corresponds to figure 14 in the paper.

python learn.py -t 2

Generalization results with everyday objects and a PR2 (figure 15 in the paper) can be computed using the below command.

python learn.py -t 3

Dependencies

Python 2.7
Keras 2.2.1
Tensorflow 1.7.0
Scikit-learn 0.18.1
Numpy 1.14.2
Scipy 1.0.1