Robot - Feature engineering on sensor data¶

The purpose of this notebook is to illustrate how we can overcome the feature explosion problem based on an example dataset involving sensor data.

Summary:

• Prediction type: Regression
• Domain: Robotics
• Prediction target: The force vector on the robot's arm
• Population size: 15001

Author: Dr. Patrick Urbanke

The data set¶

To illustrate the problem, we use a data set related to robotics. When robots interact with humans, the most important think is that they don't hurt people. In order to prevent such accidents, the force vector on the robot's arm is measured. However, measuring the force vector is expensive.

Therefore, we want consider an alternative approach. We would like to predict the force vector based on other sensor data that are less costly to measure. To do so, we use machine learning.

However, the data set contains measurements from almost 100 different sensors and we do not know which and how many sensors are relevant for predicting the force vector.

The data set has been generously provided by Erik Berger who originally collected it for his dissertation:

Berger, E. (2018). Behavior-Specific Proprioception Models for Robotic Force Estimation: A Machine Learning Approach. Freiberg, Germany: Technische Universitaet Bergakademie Freiberg.

Analysis¶

1. Loading data¶

We begin by importing the libraries and setting the project.

1.1 Download from source¶

1.2 Prepare data for getML¶

The force vector consists of three component (f_x, f_y and f_z), meaning that we have three targets.

This is what the data set looks like:

1.3 Separate data into a training and testing set¶

We also want to separate the data set into a training and testing set. We do so by using the first 10,500 measurements for training and then using the remainder for testing.

2. Predictive modeling¶

2.1 Building the pipeline¶

We then build a pipeline based on the relboost algorithm with xgboost as our predictor.

It is always a good idea to check the pipeline for any potential issues.

2.2 Fitting the pipeline¶

2.3 Evaluating the pipeline¶

2.4 Feature importances¶

It is always a good idea to study the features the relational learning algorithm has extracted.

The feature importance is calculated by xgboost based on the improvement of the optimizing criterium at each split in the decision tree and is normalized to 100%.

Also note that we have three different target (f_x, f_y and f_z) and that different features are relevant for different targets.

2.5 Column importances¶

Because getML is a tool for relational learning, we can also calculate the importances for the original columns, using similar methods we have used for the feature importances.