robotics technology - Robotic Simulation Services

Artificial Intelligence (AI) is a transformative technology. Not only can it enable autonomy and machines that can make intelligent decisions, but it can also even reinvent the technological wheels of various industries. Robotics, being an emergent technology to enable autonomy, AI is a beautiful tool that can help flourish the true capability of robotics technology. And Google's AI partner, DeepMind is reinventing robotics once again.

Today, AI is around us everywhere. From different apps to different devices/gadgets and various services we use, AI mainly integrates with these apps, devices/gadgets, or services. With this, AI provides us a superior experience of use with devices capable of making intelligent decisions and predictions. Moreover, AI is very persistent in modern life, with AI in various voice assistants, recommendation systems in services from e-commerce sites to media consumption platforms, and intelligent solutions to make predictions or autonomous decisions.

With these services and devices, AI has already become an integral part of our lives. Therefore, it is only natural that industries and companies use AI to boost their company performance on the consumer and product development and innovation front in such a scenario. One of these industries where AI has much potential is the robotics industry.

The robotics industry in itself is revolutionary, with capabilities to enable autonomy in industries. However, the endeavors of enterprises and various industries pose a massive challenge for robotics to fulfill them alone. So developers and researchers worldwide are trying to embed AI into robotics technology to usher the robotic industry to a new level.

With the help of AI, robots will not only be intelligent, but they will also be more capable and efficient. They will be able to form elegant solutions and make intelligent decisions. Moreover, they will be able to control and move a physical body which is very hard to program and build from the ground up. Furthermore, with the decision-making and prediction prowess of the system with convergence of robotics and AI, revolutionary and even unseen developments are possible.

DeepMind is reinventing robotics, and its developers have certainly caught up with this revolutionary possibility. The search giant Google's AI partner, DeepMind, is now working on this problem of convergence of AI with robotics. Raia Hadsell, the head of robotics at DeepMind, said, "I would say those robotics as a field is probably ten years behind where computer vision is." It demonstrates the lack of distinct development in robotics even when tech-like computer vision embedded in robots is already very far ahead.

The problem lying here is, though, more complex. Alphabet Inc, the parent company of Google and DeepMind, understands this daunting AI incorporation with robotics. More daunting challenges and longstanding problems remain in the Robotics-AI paradigm alongside challenges of gathering adequate and proper data for various AI algorithms to train and test them.

For instance, problems like training an AI system to learn new tasks without forgetting the old one? How to prepare an AI to apply the skills it knows for a new task? These problems remain primarily unsolved, but DeepMind is reinventing robotics to tackle the issues.

DeepMind is mainly successful with its previous endeavors with AlphaGO, WaveRNN, AlphaStar, and AlphaFold. However, with various breakthroughs and revolutionary developments, DeepMind is now turning towards these more complex problems with AI and Robotics.

However, a more fundamental problem remains in robotics. With their AlphaGO AI, DeepMind is reinventing robotics and successfully trained it through the data from hundreds of thousands of games of Go among humans. Apart from this, additional data with millions of games of AlphaGO AI playing with itself was also in use for its training.

However, to train a robot, such an abundance of data is not available. Hadsell remarks that this is a huge problem and notes that for AI like AlphaGO, AI can simulate thousands of games in a few minutes with parallel jobs in numerous CPUs. However, for training a robot, for instance, if picking up a cup takes 3 seconds to perform, it will take a whole minute to just train 20 cases of this action.

Pair this problem with other problems like the use of bipedal robots to accomplish the same task. You will be dealing with a whole lot more than just picking up the cup. This problem is enormous, even unsolvable, in the physical world. However, OpenAI, an AI research and development company in San Francisco, has found a way out with robotic simulations.

Since physically training a robot is rigid, slow, and expensive, OpenAI solves this problem using simulation technology. For example, the researchers at OpenAI built a 3D simulation environment to train a robot hand to solve a Rubik's cube. This strategy to train robots in a simulation environment proved fruitful when they installed this AI in a real-world robot hand, and it worked.

Despite the success of OpenAI, Hudsell notes that the simulations are too perfect. She goes on to explain, "Imagine two robot hands in simulation, trying to put a cellphone together." The robot might eventually succeed with millions of training iterations but with other "hacks" of the perfect simulation environment.

"They might eventually discover that by throwing all the pieces up in the air with exactly the right amount of force. With exactly the right amount of spin, that they can build the cellphone in a few seconds," Hudshell says. The cellphone pieces will fall precisely where the robot wants them, eventually building a phone with this method. It might work in a perfect simulation environment, but this will never work in a complex and messy reality. Hence, the technology still has its limitations.

For now, however, you can settle with random noise and imperfections in the simulations. However, Hudsell explains that "You can add noise and randomness artificially. But no contemporary simulation is good enough to recreate even a small slice of reality truly."

Furthermore, another more profound problem with AI remains. Hadsell says that catastrophic forgetting, an AI problem, is what interests him the most. It is not only a problem in robotics but a complexity in the whole AI paradigm. Simply put, catastrophic forgetting is when an AI learns to perfect some task. It tends to forget it when you train the same AI to perform another task. For instance, an AI that learns to walk perfectly fails when training to pick a cup.

This problem is a major persistent problem in the Robot-AI paradigm. The whole AI paradigm suffers from this complexity. For instance, you train an AI to distinguish a dog and a cat through computer vision using a picture. However, when you use this same AI to prepare it for classification between a bus and car, all its previous training becomes useless. So now it will train and adjust its "learning" to differentiate between a bus and a car. When it becomes adept in doing so, it may even gain great accuracy. However, at this point, it will lose its previous ability to distinguish between a dog and a cat. Hence, effectively "forgetting" is training.

To work around this problem, Hadsell prefers an approach of elastic weight consolidation. In this approach, you task the AI to assess some essential nodes or weights (in a neural network). Or "learnings" and freeze this "knowledge" to make it interchangeable even if it is training for some other task. For instance, after training an AI to its maximum accuracy for distinguishing between cats, dogs, and you, task the AI to freeze its most important "learnings" or weights that it uses to determine these animals. Hadsell notes that you can even freeze a small number of consequences, say only 5%, and then train the AI for another classification task. This time says for classification of car and a dog.

With this, the AI can effectively learn to perform multiple tasks. Although it may not be perfect, it will still do remarkably better than completely "forgetting," as in the previous case.

However, this also presents another problem: as the AI learns multiple tasks, more and more of its neurons will freeze. As a result, it would create less and less flexibility for the AI to learn something new. Nevertheless, Hudsell this problem is also mitigable by a technique of "progress and compress."

After learning new tasks, a neural network AI can freeze its neural network and store it in memory/storage to get ready to learn new jobs in a completely new neural network. Thus, it will enable an AI to utilize knowledge from previous tasks to understand and solve new tasks but will not use knowledge from new functions in its primary operations.

However, another fundamental problem remains. Suppose you want a robot that can perform multiple tasks and works. In that case, you will have to train the AI inside the robot in each of these tasks separately in a broad range of scenarios, conditions, and environments. However, a general intelligence AI robot that can perform multiple tasks and continuously learn new things is complex and challenging. DeepMind is reinventing robotics and now working continuously to solve these AI-Robot problems. Like DeepMind, FS Studio is also hard at work with its collective experience and knowledge over decades. FS Studio is also improving its services like Robotic Simulation Services, Offline Programming, and Digital Twins for reinventing the paradigm of robotic research and development with AI at its center.

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.

Robotics technology is continuously changing and evolving. With the development of simulation technology, the current industry is rapidly moving towards digital solutions together. With industries on the verge of shifting towards Industry 4.0, digital technologies, simulation technology, AI, AR, and VR will be the most critical pivot points. Robotic technology in industries and manufacturing processes provides vast benefits and advantages. Robot integration in production, manufacturing and other industries gives them cost savings, lower time, and efficient resource usage. Together, it all can help us to explore offline robot programming software solutions.

The traditional robotic training, testing, and operations method pales in comparison to the influx of modern demand and supply. Consequently, various industries are looking to make their supply chain from production to distribution more efficient and cost-effective. So naturally, robots are the ultimate answer for automation and efficient completion of a process with precision.

Along with the advancement of technology, industries across different fields are now looking to integrate their operations with robotic technology. However, robotic development is not an easy feat. Due to the sheer complexity of robot development and research, some industries are hesitant to adopt robotic technology. Nevertheless, the cost-benefit analysis of the development and use of robotics is simply too lucrative to ignore.

However, with the traditional methods of robot development, testing, and training, various industries cannot move towards it. Furthermore, it brought several misconceptions in the industry that robotic programming is too complicated and too resource-heavy to use. With these misconceptions, the industry was hesitant to take on the challenge. Even though offline robot programming has come a long way from its inception, misconceptions still linger around the industry with false beliefs. Words go around that it cannot handle all the variables present in real-world development or complicate things compared to the traditional approach.

Robot Offline Programming is an “offline” approach to programming that takes the robot programming away from the traditional pendant/robot cell and physical robots in production. Instead, Offline Robot Programming allows users to generate robotic instructions or code from their computers and different software solutions instead of using a robot on or taking it out of production.

The idea is simple; remove the traditional method of generating robotic instructions and code, i.e., using teach pendants and replace them with computer software or simulation software. It was necessary because the conventional process of using teach pendants or robot cells for robotic programming code was too inefficient and time-consuming. Pair this with the fact that it constrains profitability and business growth. It then becomes a throne in the process of robotic research and development.

Teach Pendants are devices that robot developers/operators use to control an industrial robot remotely. Teach pendants to allow users to manage and work with robots without connecting the device with a terminal but instead works wirelessly, i.e., no tethering to a fixed terminal. Technicians use these devices to test a robot either for programming, i.e., robotic coding or repair, or for performing some maintenance. Due to this, teaching pendants are a crucial part of industrial robotic operations.

However, using pendants is time-consuming. It's prolonged and even resource-consuming. Hence, the replacement of these traditional devices with offline robot programming software is necessary. It will allow users to operate robots either for testing or repair or maintenance or even robotic code generation with much efficiency and simplicity. Furthermore, it enables robot developers to perform all these actions through their computers without even taking out the robot from production or if it is not fully ready to be operational. Thus, it radically maximizes productivity and even reduces cycle time and downtime of the production.

Offline Programming or OLP solutions are therefore sought after by industries looking to utilize robotic solutions. Due to the advancement of simulation technology and offline programming software, it's becoming faster, more reliable, and more efficient to use OLP solutions than the traditional approach. Simulations and offline programming may differ because simulations exist without offline programming, but offline programming cannot live without simulations. Although this might make simulation and OLP seem different, they go hand in hand and sometimes are used interchangeably.

Robotic OLP can exist because of robotic simulation technology, which is currently one of the most frequent use cases of simulation technology in industries. With simulations enabling 3D representation of a robot, i.e., its digital twin, it can also represent and reproduce robotic functions, movements, behaviors, and operations in different conditions and environments. Thus, It essentially enables Robotic OLP to exist.

Although simulations make it easy to generate any environment for any use case, knowing these requirements beforehand is necessary to see the type of service one requires regarding OLP solutions. Along with this, there are some other things one needs to consider when exploring Robotic OLP.

Some of the things to consider when exploring Offline Robot Programming Software solutions are given below:

Offline Robot Programming is a technology that enables rapid programming with efficient processes and even automation in the mix. It is advantageous and beneficial to perform robotic coding for robots with complex structures, numerous moving parts, and axes or programming complex paths. These complex programming tasks generally take a massive amount of time, resources, and hard labor with teaching pendants, while it's straightforward, efficient, and swift with OLP.

OLP and Simulation technology go hand in hand. Only through the utilization of robotic simulation software can Offline Robot Programming exist. A robotic OLP is a robotic simulation software with features and functions that tests and evaluates robots in different conditions and environments of different needs and requirements around a production environment.
OLP can efficiently train, test, and evaluate robots. Hence training technicians or users for OLP is relatively straightforward. It is also very advantageous since after learning an OLP solution, it becomes easier to understand others.
Robot Offline Programming Software solutions increase the user’s productivity and the profitability of the industry/business using it. It also has a greater return on investment (ROI) relative to traditional approaches.
OLP is very useful for potential projects ideas in their quotation phase to prove their profitability and efficacy.
OLP is very efficient in tracking down any errors or faults, and potential problems in a robot before uploading programs or the robots are in operation.
Due to the nature of OLP, i.e., allowing users to program robots remotely without the need of the actual robot, it opens new possibilities for existing robot operations. Operations such as in high-mix and low-volume can explore more opportunities.
Some industries like aviation, automotive, nuclear, and space need OLP as requirements due to the nature of these industries. OLP provides massive benefits to these industries where on-site robot programming is improbable and sometimes even impossible.
OLP allows for testing in simulations through the use of various engineering tools and utilities available. Hence, it will enable testing a solution or modification thoroughly before making any actual physical modifications.
Robotic Offline Programming is now industry standard with an image of a superior software tool allowing for better returns and efficient solutions. In addition, it will enable industries to use OLP to attract high-quality staff and employees with a high chance of retaining them longer.
Due to the nature of OLP being a software tool, configurations for machines can be saved and reused on different devices instead of starting from scratch. It is a huge plus for some manufacturers/companies who have to configure large amounts of robots. Instead of having to program each robot from the start, OLP is very useful since edits and re-configuration of programs for each robot are possible.
The use of OLP also attracts other prospects like Robotic Simulation Softwares for the design, research, and development of robots. It is a very efficient and beneficial solution that allows for rapid growth, testing, and prototyping of robots at lower costs, less resource usage, and better efficiency.
Offline robot programming is a huge win when put in comparison with traditional ways of robotic programming. Allowing for a far quicker robot deployment or installation, OLP allows for rapid testing, fine-tuning, and programming a robot in a virtual environment in just days when the traditional method may even take weeks.

Furthermore, with virtual environments to teach the robot, downtimes are no longer present when teaching the robot a new programming or operation path. OLP can even upload new programming in the robots when in a live production environment or when it is operating. Apart from these, safety, quicker cycle times between teaching the robots, or a straightforward approach to test a new configuration, all are easier to perform through OLP.

Various companies like FS Studio provide OLP solutions to make it easier for companies/industries/manufacturers to adopt OLP solutions in their existing production environment. FS Studio provides Robotic Simulation Solutions crucial for OLP solutions with a decade of collective knowledge, experience, and skills in store. It helps the production team to focus on the actual product rather than shift their resources in offline robot programming implementation and adaptation. Nevertheless, OLP is a technological innovation that will help productions reach new levels of innovation with more possibilities and opportunities to explore.