# Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing
## 6 Challenges and Open Issues
### Use of Simulations and Synthetic Data  
The use of simulations and synthetic data can facilitate the development of smart industrial robotic systems in various ways and stages. Generating low cost and large amounts of data, accelerating the design cycle whilst reducing costs, and providing a safe and fully controlled testing environment are only some of the opportunities [112]. However, these benefits come with several challenges that are yet to be solved. In the context of the reviewed smart industrial robot control methods, the challenges are highly connected to differences between simulations and real-life, which therefore usually decreases the precision of the system when deployed in real-life scenarios. Physic simulations, object virtual representations, sensor data recreation, artificial lightning and other countless bits of real environment all contribute to the issue of transferring learned models from simulation to reality. Collection and processing of data for machine-learning tasks play an important role and smart industrial robot control is not an exception as ML-based approaches are widely used to solve challenging tasks. According to [113], on average, more than 80% of time spent on AI projects is based on the collection and processing of the data. The data collection techniques can be distinguished using several methods. The reviewed techniques in [114] vary for different use cases. For example, in the field of autonomous driving [115] or in more traditional applications, such as household object recognition, people detection or machine translation, there are openly available datasets that could be reused and adapted of specific needs for model training [116]. Due to specific elements of smart manufacturing and precision requirements, a different situation can be seen in this field. The product variety is changing more quickly and algorithms have to be trained on new data sets. Therefore, the re-usability of existing data sets is fairly low. Manual labeling methods cannot meet the requirements of agile production as it is time-consuming, expensive and usually requires expert knowledge of the specific field, and thus, human error can arise in the labeling process [117]. These aspects also conflict with the goal of smart industrial robots to automate tedious jobs that lack creativity. Introducing the smart industrial robot control methods that require manual labeling just shifts the manual work to a different sector. Synthetic data generation, on the other hand, decreases the time required for the labeling process by the use of physics and graphics engines to replicate real-life situations of environmental uncertainties. In a controlled environment and in non-complex scenes listed in Section 3, the benefits of synthetic data usage might not overcome the reality-gap issues. However, generalizing the knowledge requires vast amounts of data and when it comes to many different environmental parameters and complex scenes, all the variables can be taken into account in the generation process. Moreover, combining real and synthetic data could leverage between efficiency of the data collection process and the precision of the trained model, but yet lacks comprehensive studies in manufacturing applications. Moreover, in scenarios where robots learn by themselves (see Section 4), there is also a possibility of training the robots in real life whilst interacting with the real environment and using the feedback from it to learn policies. Similarly, this can be tedious, expensive and very time-consuming work [118]. Additionally, in this case, the robot needs to actually execute actions that in the first learning iterations can be dangerous to the environment and the robot itself while it is exploring the action possibilities. To decrease the risksthat can arise by random exploratory actions, the training of the robot is mostly moved to digital space.