How Robots Learn From Each Other
Tags: Federated Learning, Robots, Artificial Intelligence, AI, Machine Learning

How Robots Learn From Each Other

Image by Barbara A Lane from Pixabay

Robots learn from each other

COMAU is an automation provider and its robots are installed in dozens of plants, in the automotive domain. In this context, these industrial automation customers are not eager to share their data.

Nevertheless, those data are precious for the robot maker with regards to both business (new added value services for customers) and technical aspects (to improve robot performance and quality).

Robot joints contain a belt that naturally loses its elasticity over time. With time and usage, the belt tensioning changes and actually, in order to prevent failures caused by a wrong tension, operators have to regularly check the belt status with a manual maintenance intervention. These operations require time, effort and eventually a production stop. Moreover, these manual tasks bear the risk to be useless if the belt status is still good.

The identified solution implemented for this case is a Machine Learning model able to predict the status of the belt based on the analysis of a set of specific parameters. In order to achieve a solution that provides a reasonable quality of the prediction, it is extremely important to identify the proper training set to use.

Given that the same kind of robots may be operating in different plants and under control of different owners (customers of COMAU), it is possible to assume that: (i) each one of the customers may be interested in running the same predictive ML model for those specific class of robots so as to increase their efficiency; (ii) each one of the customers may face the issue of not having enough training data to produce an effective Machine Learning model suitable for the production: (iii) customers are not keen to open their data to third parties.

In a similar scenario, the MUSKETEER platform comes into play enabling data sharing in a privacy preserving way for COMAU and its customers and thereafter with the possibility to use all available training data to build a classification model based on federated machine learning.

To test the MUSKETEER platform functionalities and in particular the MUSKETEER Client Connector, two testbeds called RobotBox have been setup. The first RobotBox (from now on called “real RobotBox”) is located in COMAU headquarters in Turin and the second one (the “virtual RobotBox”) is a virtual simulator hosted in a ENGINEERING facility in Palermo. Both testbets are simulating two different customers plants.

Fig.1 RobotBox testbeds used for the experiment.

Defining quality data

Going a bit more into the details, the RobotBox replicates an axis of a COMAU robot. It is composed of a motor, a belt, a gearbox reducer and a weight (yellow part in the Figure 2 above). In order to collect data at different belt tensioning levels, we had to space out the motor and gearbox from each other. To do this we installed a slicer to move the motor and a dial gauge to measure the distance between the two parts. We have decided to consider 3 different belt tensioning levels.

The RobotBox always performs the same movement, called cycle. After each 24 second-cycle, we collected signals from the RobotBox motion controller. For each cycle we calculated 141 features describing the different signals aspects (e.g. mean value, maximum, minimum, root mean square, skewness, integral, etc.). Those features have been chosen based on the knowledge and literature about belt tensioning.

For each level, we have performed around 6000 cycles, for a total of 18000 samples for each RobotBox, considering the 3 different belt tension levels. For this scenario, we have chosen to train a classification model using an artificial neural network.

COMAU plays the role of the aggregator for the federated machine learning task and for this use case also the role of the first participant. On the other side, ENGINEERING plays the role of the second participant. So, COMAU (located in Turin) created the task choosing the MUSKETEER Privacy Operation Mode 1 (POM1), where data cannot leave the facilities of the participants, the predictive models are transferred without encryption and the artificial neural network as algorithm. Moreover, the maximum number of iterations was set to 300, the learning rate to 0.00015. The architecture of the model shaped as a neural network is a 3 layer-neural network with 48, 16 and 3 units tuned in the preparatory phase of this evaluation.

After the task creation, including validation and test data, COMAU joined the federated process as participant that provides the training data of the real RobotBox. Finally, ENGINEERING joined the same task providing the training data of the virtual RobotBox.

Federated data sharing is better than playing alone

Looking at the right side of the diagram below, it’s possible to see how the overall accuracy obtained by the trained model, thanks to the federated task, with both RobotBoxes is 89% and the related confusion matrix is very diagonal. On the other hand, on the left side of the figure, the accuracy of a model trained only with the data of the real RobotBox or only with the data of the virtual RobotBox is lower, respectively 86% and 81%.

Fig. 2 Comparison of the non-federated and federated approach.*

Davide Dalle Carbonare, Industry and Security Technologies, Research and Innovation (IS3) Lab Engineering Ingegneria Informatica spa

Chiara Napione, Digital Innovation Infrastructure, COMAU Spa

Lucrezia Morabito, Digital Innovation Infrastructure, COMAU Spa

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824988.

*Fig. 2 has been updated on 18/12/2020. It contained an error that has been now corrected.