Synthetic data generation techniques for robotics applications

9th May 2023
Paige West

The major advancements in machine learning over recent years have seen industries such as robotics come on leaps and bounds in their development.

Algorithms have been created that allow computer programs to learn and understand various stimuli at a rate far quicker and to a more intrinsically accurate level than a human could ever process, these programs need to be fed with new information to continue the algorithmic development – this is where synthetic data comes into play.

Large data sets are needed to be able to adequately train and enhance the performance of machine learning, but it is not always possible or cost-effective to use real-world data. Synthetic data sets can be created at scale to mimic the characteristics of real-world data and can also be refined to have a higher accuracy in data labeling with fewer anomaly errors.   

Techniques for synthetic data generation

There are three main techniques for generating synthetic data:

Generating data according to a known distribution

In circumstances where a real-world data set does not exist, but the analyst has a good prior knowledge and understanding of the required dataset's key features and characteristics, a random sample of data can be created to fit the expected distribution model.

This technique is best suited to simplistic data as the overall usability of the data depends on the analyst's background knowledge of the specific topic and can include a high bias or error level. 

Fitting real data to a known distribution

If real-world data is available, then AI can determine the best-fit distribution model and synthetic data points can then be produced to fit those distribution trends. This technique of data generation often employs the Monte Carlo method, while this method is good for some calculations, it is very simplistic algorithmically and potentially inaccurate compared to other synthetic data generation techniques.

Neural network technologies

Neural networks are a type of deep learning technology that can generate an unlimited supply of artificial data on a much larger scale than more traditional techniques and algorithms. Rather than just being able to process simple tabular-based data, neural networks can also process and manufacture data such as images. Some examples of generative neural networks are:   

Generative Adversarial Networks (GAN) – GANs use two sub-models to work against each other to verify the data it produces. The first model is the generator which is used to create the synthetic data, and the second is the discriminator, which analyses whether the data produced looks authentic or fake.

The discriminator side of the network is trained on real-world data and feeds its analysis of the synthetic datasets accuracy back to the generator. The generator then learns what needs to be fine-tuned, and the two models continue the process/cycle until the generator produces data realistic enough that the discriminator can’t differentiate it from the real-world data. 

Variational Auto-Encoder (VAE) – VAEs are capable of learning a compressed representation of a given dataset, that can then be used to generate new data points similar to the real-world dataset.

They use a probabilistic model to create new data points, which allows them to produce a wide range of outputs that mimic the original data’s features and dependencies. VAEs can be a powerful tool for generating complex synthetic data such as image renderings (including realistic faces and handwriting) as well as complex tabular data.

Applications of synthetic data in robotics

The use of synthetic data can be beneficial for many sectors but its use in robotics could be truly transformative. Robotics programming can be trained much more quickly and efficiently with synthetic data as opposed to real-world data, allowing them to become more accurate in the operations they carry out.

By using synthetic data alongside real-world data, it’s possible to create realistic simulated environments to make training and tests more reliable.

There are various applications of synthetic data use in robotics, for example:

Object detection & recognition

In manufacturing settings, robots can be used for quality-checking items coming off of a production line thanks to object detection and recognition training. Items can be scanned and checked to see if they fall within the required parameters for everything from size to colour – if a flaw or an anomaly is detected that item can be rejected.

Motion planning & control

Motion planning and control is a technology that is imperative to the development of the robotics industry in a range of sectors. The aim of motion planning is to provide a robot with a safe route from its start point to its end point, avoiding obstacles, collisions, or dangers.

To understand motion planning and control in its simplest form, we can look at a device that has been around for years – the robot vacuum. These robotic cleaners ‘map out’ the layout of a house to avoid falling downstairs, moving around furniture, and running into walls. To expand that idea onto a larger scale, we can look at the rise in driverless cars – they have to monitor the environments around them and make thousands of quick decisions (control reactions) to maintain the safety of the vehicle and passengers.

Future directions and challenges

Employing the use of synthetic data in the robotics industry is needed to see the development required to advance the industry. The more data that robotic devices and programs are fed, the more their algorithms can be trained to recognise certain features and characteristics of the data, making them much more reliable and efficient at their jobs.

While the benefits of using synthetic data are easy to see, it is important to consider the potential challenges it could also bring. One such challenge is accuracy – as with most things, the resulting output relies on the quality of the input. If inaccurate, biased, or anomalous data is fed into a neural network, the synthetic data it produces will have replicated and even possibly compounded those errors. To avoid this happening, certain best practices should be followed when assessing real-world data to ensure that it is as accurate and representative of the required situation and stimuli as possible. 

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