AI - Robotic Simulation Services

Industries are rapidly advancing. With growing adaptation and accessibility of state-of-the-art technologies, various industries’ production innovation and R&D technology are becoming very advanced, albeit more complex. However, with technologies getting more complex, they are also getting easier to adapt. So laden with numerous possibilities and opportunities, industries are adopting digital technologies in their industrial application to reap these lucrative advantages as deep learning boosts robot picking flexibility.

The ultimate pursuit of automation in industries and production goes through the path of intelligent and smart robots. With more demanding industries, newer and better robots can perform various industrial applications more smoothly and efficiently. But as industries expand their reach into more fields/sectors, they need robots to achieve even more different tasks in different environments.

This broad spectrum of need for the usability of robots leads to robotic technology not being able to keep up with the demand. Hence, traditional methods and approaches to robotics must be let go to introduce new and better techniques to robotic technology. Within the advent of digital technology lies more possibilities for robotics that are even unseen before.

Digital technologies and platforms like Robotic Simulation Services, Offline Programming, Augmented Reality, Virtual Reality, and Artificial Intelligence take the world by storm. They are now in integration or development for almost every industry possible. The robotics industry also is not lagging in this aspect, with robotic manufacturers or various services providers already utilizing these technologies to propel robotics further. Deep learning is one of the technologies in use, with much anticipation and exciting possibilities, within the robotic industry.

Let's talk about Deep Learning

Deep learning is a type of Artificial Intelligence, or more so a kind of Machine Learning approach. In the broader AI paradigm, Machine Learning is a subset of AI that refers to an AI system that can learn with the help of data instead of developers having to code it. ML is an approach to AI that enables various algorithms to remember from data, i.e., training data consisting of input and output data, to infer a pattern or a “knowledge” in the input data about the output. With this knowledge, ML algorithms can effectively predict the outcomes with the analysis of input data.

Deep Learning is a similar approach. It's a family of algorithms in the machine learning paradigm based upon Artificial Neural Networks (ANNs). These ANNs in deep learning can perform representation learning. Representation learning is a method in which systems detect or infer a pattern or representation, i.e., features in the input data for feature detection or classification. Hence, computer science also defines it as feature learning since it detects features from raw data and uses them to perform some specific task.

How Deep Learning Boosts Robotic Picking Flexibility

Deep learning boosts robotic picking flexibility with its data by effectively imitating how intelligent creatures like humans gain knowledge and do certain things. In deep understanding, a system takes in input data and tries to infer a pattern or detect some specific feature in that data. This “learning” approach is known as deep learning. Furthermore, education can also be either supervised, unsupervised or semi-supervised.

These are various deep learning architectures that researchers combine up with various other computer techniques and technologies to enable different features and functions in robotics: deep neural networks, recurrent neural networks, convolutional neural networks. Deep reinforcement learning and deep belief networks are various architectures in deep learning—robotic technology pairs up these architectures with different hardware and technologies to build various robotic functions.

For instance, robotic researchers and developers use convolutional neural networks for computer vision with cameras and other sensors to give visual information like depth. Likewise, different architectures enable different computer application fields like speech recognition, natural language processing, image analysis, bioinformatics, etc. Moreover, these applications are often in use for various purposes within other industrial areas.

Why Deep Learning Boosts Robotic Picking Flexibility?

In robotics, one of the most complex things to perfect is its ability to pick things up. For human beings, picking items seems very easy. However, seemingly effortless things with biological creatures are not always similar to robotics and computer systems.

Thus, although it may seem that picking items up is easy, it is not the case. The complex interworking of different systems together to perform even a simple task is very hard for computers. For instance, to first pick things up, you need to know what you are picking.

This part is usually straightforward since, for example, you can tell a computer that the stuff it's gathering is in a specific location. But the hard part comes when it's doing the actual picking. For example, how is it even going to pick the object? Even in a single production environment, there are a variety of things with different shapes and sizes. In addition, objects have different textures, structures, and a specific suitable picking spot.

We can undoubtedly program a robot to utilize information about a particular object and a suitable method to pick the thing, but programming it to select it is challenging. Relatively, programming a robot to choose only a single type of object can be easy, but you would need other robots for different kinds of things/products. So this is certainly not an effective method to accomplish this.

Furthermore, products and objects may behave differently in different environments, creating complexities in ways deep learning boosts picking flexibilities. For instance, a product with a smooth surface can be slippery to grab or hold onto in a humid environment. Moreover, picking other objects in different backgrounds requires the robot developer to program the robot for various environments and various things. Along with this, considering the wide range of products, this problem quickly becomes substantially huge.

One of the enormous complexities we are not even exploring yet remains motor skills. Programming a robot to perform specific motor skills and functions is one of the vastest complexities of the robot development paradigm. Even to grant them specific motor functions is very hard. That's why it's a huge deal, even if a robot can perform simple tasks like holding a cup, walking, etc. However, now you can certainly deal with these problems through various means.

For instance, a robot that needs to move can have wheels. A robot that does not have to move but grab onto things can have arms on a fixed body. But these solutions are also tough to implement. Add this to the use case, such as a moving robot that has to move on an uneven surface or a wrong road or even locations where there are no roads, i.e., hills, rocky places, etc. Then this problem becomes substantially more challenging. Similarly, for industrial robots, picking different products and objects is also a complex problem due to different environments and types of things it has to deal with in a particular manner.

Apart from these problems, one primary concern is how deep learning boosts robotic picking flexibility, computer vision. A robot needs to see the object it's picking up. Recognizing a thing insight is a significant feat of computer vision that is currently possible with a massive range of solutions available. But simply recognizing an object is enough to interact with the thing. The robot has to know what object it's looking at and determine how it will pick it up. It again involves problems regarding the size, shape, texture, and structure of the object or product.

In hindsight of all these problems, an industrial robot capable of gripping and interacting with different types of objects or products with other characteristics and properties in different conditions or environments is tough to build. Consequently, it is one of the biggest problems in the industrial robotic plane. It is where deep learning comes into play.

We can use various deep learning techniques to teach a system to recognize and interact with an object. Using deep learning methods, we can use data from multiple production sites, companies, and industries of interaction and manipulation of various things and products for training the system. This data can effectively help a deep learning model to “learn” how to pick different objects in different environments in various particular ways.

The initial data can come from systems already proficient in picking and dealing with objects, which would help in how deep learning boosts robotic picking flexibility. For instance, there is data with humans picking up things. These specialized robots pick only a specific object or interact with them, or even human operators that operate machines to pick up different objects. After data collection of these types, a robot with a deep learning system can go through a training process to effectively learn how to replicate the task or perform it more efficiently.

With this, data collection is complete from a specific specialized robot and for different machines. Moreover, developers and researchers can share and augment such data for training there be used robots for broader use cases and even interact and manipulate objects they are yet to interact with. The possibilities are endless as deep learning boosts robot picking flexibility. As a result, developers can build with a wide range of picking flexibility that can help an industry drive itself towards the end goal of automation. It is why companies like FS Studio provide various services regarding robots and AI tools like deep learning. With decades of collective experience and knowledge with a wide range of expertise, FS Studio provides deep learning services for various robots and other innovative services like Robot Simulation Services, Offline Programming Solutions, and the integration of innovative technologies like AR and VR in different systems.

The landscape of Robotics technology is evolving, pushing industries forward for a 360-degree approach to robotics. More so than before, today, robotic technology is progressing at a swift speed alongside its integration with technologies like Artificial Intelligence (AI), Simulation technology, Augmented Reality (AR), and Virtual Reality (VR). Robotics was always at the center of a future where industries are digital with automation at its core. However, industries that fully integrate AI and digital technology to enable automation with robots are still far away.

In the current world, car production and manufacturing is probably the industry with the highest level of robotic usage. One of the most prevalent uses of robotics and automation even in this industry is the Tesla manufacturing facility. Even though this is the case, Elon Musk, the CEO of Tesla, admits that robots are tough to automate and efficiently run without advancing digital technologies like AI and more innovative technologies like the Offline Robot Programming Software Platform or Robotic Simulation Services.

However, with the advent of Industry 4.0, the next industrial revolution, we will see some industries take a 360-degree approach to robotics through digital technology. Robotics technology is a crucial part of this transformation. Hence, enterprises will have to change their traditional policy to robotics with a new innovative and modern digital strategy to keep up with the changing industry and competitors.

With that said, industrial robotics is complex, in fact, very hard. With industries and production, the site the robots will have to work in is susceptible to all kinds of risks. These risks are not only limited to humans but also to the industry itself. Production environments generally contain various types of materials and substances that can create many unforeseen circumstances and problems. For example, rusts or corrosion of machine parts or robots, leaks, noise pollution, etc., are issues that the production will have to deal with almost regularly. Pair this with unforeseen problems in machines since they run all the time; industrial environments are very tough for robots to survive, which is why the 360-degree approach to robots is so important.

Not just the risks and problems for the robots, but the aftermaths of these problems and faults are more expensive to a production site. For instance, when a robot fails, or an installation of a new robot occurs, the actual production environment will probably suffer from its downtime. And industries do certainly not like downtimes. Downtimes lead to the stopping of whole production facilities and bar the production, resulting in the loss. Furthermore, this loss becomes more substantial if the materials or products that are not complete can go wrong. It will add the loss of materials and incomplete products to lower numbers of outgoing products from the factories.

Robotics in industries possesses more importance when it comes to error detection. Since production sites and factories can be dangerous and harmful for humans since they have to approach the machines to detect errors, it can be hazardous and even fatal in some cases. Hence, the emergence of drones and locomotive robots is rising in this department. However, industries are still taking the old approaches to use robotics and digital technology.

Industries generally shape robots around the production and use cases in the production sites rather than the inverse. Although typically, enterprises approach robotics as only a medium to replace human resources either in potentially dangerous places or tasks that may not be possible for humans to perform, the 360-degree approach to robotics in the future would only develop the technology further. Instead of this, industries and production facilities should shape themselves around robotics. Of course, it does not mean changing the particular industries’ end goal towards robotics and its implementation. Instead, it means to shape the industry so that it embraces robotics and involves it in the actual process and communication of the production sites.

Usually, robots in industries are linear, i.e., they are put in place of a human to speed up a process/task with a set of inputs fed to them by the developers or operators. They only do or set out to do specific functions inside the production line.

For instance, we can use a robot to put a product inside a box, put product stickers in packages, and seal the box. However, these robots only perform one task, i.e., a robot for placing products in a box cannot close it or put product stickers on it. Therefore, it limits the opportunities and possibilities that robotics can unlock. For instance, with the integration of technologies like AI, robots can become more dynamic and a part of the actual production process rather than the production line.

With AI and technologies like simulation, innovations like Offline Robot Programming Software Platforms are possible. With this, robots become more helpful; they can even participate in production processes to make them brighter and effective. Moreover, With the possibilities of self real-time optimization and self-diagnosis possible, robots will become able to report errors or possible errors in the future and solve those problems faster than humans ever can. And the time essential for robots to process what went wrong and determine if a possible solution is tiny.

In comparison, humans must first come across the errors, either after the error has already happened or detect it beforehand. Then such errors have to go through actual experts and need proper analysis. Only after this, a solution can come up which can fix the problem. But, unfortunately, the developers or the debug team may misinterpret the answer due to insufficient data or enough time. Even during this time, though, the situation can escalate, sometimes even forcing a downtime in the production. But the upcoming 360-degree approach to robotics would change it all.

With the integration of robotics from the start, alongside the significant goals of the particular industry, the actual use cases of robotics with more comprehensive and newer possibilities can emerge. It will let the industries access the actual use case they want from robots and the robotic technology more appropriately instead of focusing on what robots can do afterward, limiting the robotic possibilities. Only after integrating robotics with the actual goal or vision can an industry properly access what they need from robotics and other complementary technologies.

Every industry has a different need. Along with this need, various production systems and methods emerge. Hence, every industry or company may need something different from robotic technology. Even without using the latest or bleeding-edge technology, a company may fulfill its actual needs, i.e., every company need not use them. Hence, every industry needs to use and approach robotics differently to achieve their needs.

For instance, in a data-driven industry, the static robots that cannot communicate or process does not make sense. Since it's a data-driven industry, utilizing such technology in their robots will provide them with numerous benefits.

In an industry where robots and humans have to work together, human-robot collaboration makes much sense for the upcoming 360-degree approach to robotics. For instance, to perform a task like inspection of a faulty machine, robots can collect data from the air or the ground, while humans can analyze them and provide their insight. It becomes even more efficient with technologies like digital twins, AR, or VR.

3D models with digital twins can be much more efficient if industries integrate them with robotics. Automation becomes much closer while remote operations can thrive. With simulation technology, the training and testing of robots will become a digital endeavor rather than an inefficient, risky and expensive physical approach. Digital technology for robotics can enable rapid prototyping, higher form of product innovation, more advanced Research and Development (R&D), all the while remaining inexpensive, safe, efficient, and fast.

The 360-degree approach to robotics would also impact how we teach the robots as well. Technologies like offline robot programming (OLP) will enable robotics to evolve more rapidly. Offline robot programming replaces the traditional approach to teaching robots with Teach Pendants. Teaching pendants can be very slow, inefficient, and resource-consuming on top of being a significant cause of downtimes when it comes to teaching a robot. Pendants require robots to be out of production and in teaching mode the whole time during their programming. It increases downtime during the installation of robots and brings downtimes if the production house wants to upgrade the programming or coding.

But OLP replaces all that with a software model of teaching. The generation, testing, and verification of the teaching programs are possible through software simulations through OLP. OLP effectively eliminates the need to take out robots during its teaching process, allowing production to continue and robots to work even when training. OLP even opens a path for rapid maintenance, repair, and continuous upgrading of robots, all due to its teaching possible through software updates. Along with this, adopting simulation technology is another major win in terms of robot research and development. Simulations with AI can enable whole new ways of robot development, testing, and deployment. Pair this with technologies like Machine Learning, deep learning, and digital twins, AR and VR. Robots will then indeed be able to thrive. Companies like FS Studio that thrive in product innovation and advanced R&D technology can provide the industry with a much-needed push to propel themselves towards Industry 4.0. With over a decade’s collective knowledge and experience, FS Studio delivers a plethora of solutions for robotic technology and helps companies take a 360-degree approach to robotics.

Combining simulation and AI technologies like Machine Learning & Deep Learning unveils outstanding new possibilities and opportunities. Moreover, the use of AI on traditional approaches to simulation may even bring forth a paradigm shift in the industry regarding how we perceive and develop the simulation.

Although simulation and Artificial Intelligence (AI) are two different technology paradigms, these technologies are related to each other in their primary forms. In computer engineering, simulation imitates an environment or a machine, while AI effectively simulates human intelligence.

While they may be related, simulation and AI were being used very differently with different mathematical and engineering approaches. However, in recent years, the development of AI-based simulations has experienced rapid growth in various industries.

For instance, now infamous, Cyberpunk 2077 used AI to simulate facial expressions and lip-syncing in the gaming industry. On the other hand, Microsoft Flight Simulator 2020 used AI to generate realistic terrains and air traffic.

The power of AI to enable rapid simulation development with faster, more optimized, and less resource-hungry simulations even on a large scale would empower more applications of simulation technology in far wider industries and platforms.

However, to understand the benefits of using AI in simulations and its development, we need to understand the traditional simulation development approach and its use in this scenario at first.

Traditional Simulation vs. AI-based Simulations

The basic idea behind simulation development is to gather data related to the machine, environment or anything for different inputs and conditions. These data would then be collected, analyzed, and studied to understand how the machine/environment/anything simulated functions and behaves under different conditions and situations.

This understanding would then be used to build a basic mathematical model that can govern and imitate the actual object in different conditions, then used to construct a simulation model that can replicate or simulate the real thing.

However, when AI is used to build these simulation models, the AI has to be fed with data related to the object/environment's behavior and how these subjects (object/environment to be simulated) function under different conditions and settings. During this process, the AI model requires relevant data that can be considered a sample of the simulation subject and represents the subject properly.

Generally, Neural Networks (NNs) would be used as the AI model to be trained. After the training, this would simulate the subject and its behavior.

Both approaches, either traditional approaches or AI for simulation, have their advantages and disadvantages. One of the significant advantages of the conventional simulation method is that the mathematical model defined after studying the simulation subject can be reused and reconstructed easily.

This allows other development teams to verify or reuse the same mathematical principles or models to generate the simulation. A traditional approach would also enable the developers to expand the simulation based on their understanding of the subject without explicit testing or proof test.

One of the significant disadvantages of this traditional approach remains to be its complex and resource-hungry process to generate the simulation. This is because everything has to be done by the simulation developers, who would also have to be experts in respective domains such that they need to understand the subject very closely.

Meanwhile, in AI-based simulation development, data is one of the essential components. The subject's information needs to be in abundant amounts and deterministic such that the data can represent the subject very closely.

This type of data may not be available readily when the data needs to be either collected or generated. But after the collection of accurate and abundant data, an AI-based/aided approach is very advantageous since there is no need to understand the subject by developers themselves.

Another significant advantage of the AI-based simulation holds within the power of AI to discover patterns or behavior in subjects not even considered or found by the developers. Apart from this, training an AI model usually takes a lot of time, but it may not be as resource hungry, complex, and costly as the traditional approach.

One of the significant disadvantages of the AI approach is that the model builder cannot be recognized or understood by developers in any way, so it cannot be usually reconstructed unless similar data or input is fed again to train the model.

Apart from this, due to the data required to qualify the model, expanding the model will generally be impossible without sufficient data.

Combining Simulations and AI

Using AI in simulation generation or development would enable data-powered development with rapid changeability and minimal human involvement. Although the simulation traits would be considered too complex for humans to develop, AI may easily reconstruct such characteristics if sufficient data is provided.

Due to this, AI can be used to simulate something too hard, complex, or time-consuming for humans in a short time without too much effort. Thus, not only would the development of simulations be faster, more productive, and easy, but AI would also enable the rapid iteration and tweaking of simulations that would be far less feasible, especially on a large scale.

We can open new doors by combining the power of AI and simulation for product design and development. Generally, without AI simulations, developers have to design a product/model that must be intensively tested before production, and changes are needed after the story. Then, the same process would have to be repeated.

This process is very resource-intensive. But through AI, design changes and validation can be easily tested through simulation, enabling rapid iteration and development.

The development and adoption of AI for simulation are far more required in industries like Augmented Reality (AR) and VR (Virtual Reality), where the sheer complexity of building high scale models, environments, and graphics through the traditional methods would be infeasible compared to using AI to develop and deploy simulations with its data-driven approach of development. The opportunities in AR and VR could be far more explored and matured through the AI to generate and develop simulations.

Alongside this, simulation of subjects like fluids (air and water) is brutal to construct with only a traditional approach, the result of which would still not be good and very close to reality. But with the help of AI, such simulations would be closer to reality and more refined.

One of the significant advantages of AI-based simulation compared to the traditional approach is that the conventional system would be significantly resourced heavy since it usually calculates each simulation particle.

However, AI-based simulation would enable such complex simulations easily since AI can perform these calculations/predictions much faster and less resource hungry. Alongside this, generative simulations like the generation of models, terrains in games, and product designs would also be possible with AI.

For instance, take the game Microsoft Flight Simulator 2020 as an example. This game allows gamers to experience realistic flights worldwide without lagging in the quality of models, terrains, and environment.

By traditional approach, this would mean that the game developers would have to model and build all terrains used in 3D along with matching landscapes and backgrounds to give the simulation a realistic feeling.

This would have cost the game developers a massive amount of time, resources, and a considerable number of experts to deal with complex problems lying ahead in such an enormous project. Realistically, such a project would not be feasible or even practically be possible to complete.

But through the use of AI, the developers used massive amounts of data that are already available and combined them with vast amounts of computation through the power of the cloud to train an AI model that could build realistic 3D models of terrains, environments, along with grasses, trees, and water-based upon the real world.

The results produced were pretty spectacular and received substantial critical acclaim from game developers and gamers alike.

Conclusion

By combining simulations and AI, we can unfold new opportunities and endless possibilities in different industries.

Along with technologies like Machine Learning and Deep Learning, AI-enabled simulations will be propelled by the data-driven backend. Conquering the disadvantages of the traditional approach to simulation, AI-based simulations will be able to push the boundaries of what simulations can do.

Even the most complex simulations, which would be next to impossible when developed with traditional methods, will be attainable by combining simulations and AI.

Moreover, with AI enabling rapid development of more optimized and improved quality, the industry may experience a revolution empowering next-level simulations with realism and details never seen before.