robotics industry - 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 chip giant NVIDIA and Open Robotics partnership may mark a significant stride in the robotics and Artificial Intelligence industry.

NVIDIA is one of the most potent entities for chips manufacturing and computer systems, along with Open Robotics being a giant in the robotics space. This partnership brings these two giants together to develop and enhance Robot Operating System 2 (ROS 2).

As put forth by Chief Executive of Open Robotics, Brian Gerkey, users of the ROS platform were using NVIDIA hardware for years for both building and simulating robots. So the partnership aims to ensure that ROS2 and Ignition will work perfectly with these devices and platforms.

ROS is not a new technology. From its inception in 2010, ROS has been a vital source of the developmental platform for the robotics industry. Also supported by various big names like DARPA and NASA, ROS is an open-source technology that combines a set of software libraries, tools, and utilities for building and testing robot applications. ROS2 is the new version with many improvements upon the old ROS and was announced back in 2014.

However, Open Robots’ Ignition simulation environment primarily focused and targeted the traditional CPU computing modes over these years. Conversely, on the other hand, NVIDIA was pioneering and developing AI computing and IoT technology with edge applications in their Jetson Platform and SDKs (Software Development Kits) like Isaac for robotics, NVIDIA toolkits like Train, Adapt, and Optimize (TAO). All this simplifies AI development and deployment of AI models drastically.

NVIDIA was also working on Omniverse Isaac Sim for synthetic generation of virtual data and simulation of robots. Jetson platforms are open source and are available to developers. But now, with its combination with the Omniverse Issac Sim, developers will be able to develop physical robots and train them using the synthetic data simultaneously.

The NVIDIA and Open Robotics partnership majorly focus on the ROS2 platform, and it’s boosting its performance on the NVIDIA Jetson edge AI and its GPU-based platforms. The partnership primarily aims to reduce development time and performance on various platforms for developers looking to integrate technologies like computer vision and Artificial Intelligence (AI) and Machine Learning (ML), and deep learning into their various ROS applications.

Open Robotics will improve data flow, management, efficiency, and shared memory usage across GPUs and other processing units through this partnership. This improvement will primarily happen on the Jetson edge AI platform from NVIDIA.

This Jetson Edge platform is an AI computing platform and is mainly a supercomputer-based platform. Furthermore, Isaac Sim, a scalable simulation application for robotics, will also be interoperable with ROS1 and ROS2 from Open Robotics.

The NVIDIA and Open Robotics partnership will work on ROS to improve data flow in various NVIDIA processing units like CPU, GPU, Tensor Cores, and NVDLA present in the Jetson AI hardware from NVIDIA. It will also focus on improving the developer experience for the robotics community by extending the already available open-source software.

This partnership will also aim that the developers on the ROS platform will be able to shift their robotic simulation technology between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics. It will enable these developers to run even more large-scale simulations with the enablement of even more possibilities. As put by the CEO of Open Robotics, Operian Gerkey, “As more ROS developers leverage hardware platforms that contain additional compute capabilities designed to offload the host CPU, ROS is evolving to make it easier to take advantage of these advanced hardware resources efficiently.”

It implies that developers will openly leverage processing power from different hardware platforms with more powerful, low-power, and efficient hardware resources. So, for example, ROS can now directly interface with NVIDIA hardware and take its maximum advantage, which was hard to do before.

The NVIDIA and Open Robotics partnership also put forward possibilities of results to come out around 2022. With a heavy investment of NVIDIA towards computer hardware, modern robotics can now utilize this hardware for enhanced capabilities and more heavy AI workloads. Furthermore, with NVIDIA's expertise in inefficient data flow in hardware like GPU, the robotics industry can now utilize this efficiency to flow large amounts of data from its sensors and process them more effectively.

Gerkey further explained that the reason for working with NVIDIA and their Jetson Platform specifically was due to NVIDIA’s rich experience with modern hardware relevant to modern robotic applications and efficient AI workloads. The head of Product Management, Murali Gopal Krishna, also explained that NVIDIA’s GPU accelerated platform is at the core of AI development and robot applications. However, most of these applications and development are happening due to ROS. Hence it’s very logical to work directly with Open Robotics to improve this.

This NVIDIA and Open Robotics partnership also brought some new hardware-accelerated packages for ROS 2, aiming to replace code that would otherwise run on the CPU, with Isaac GEM from NVIDIA. These latest Issac GEM packages will handle stereo imaging and color space conversion, correction for lens distortion, and processing of AprilTags and their detection. These new Issac GEMs are already available on the GitHub repository of Nvidia. But it will not include interoperability between Isaac Sim from NVIDIA and Ignition Gazebo from Open Robotics as per expectations of it arriving in 2022.

Meanwhile, though, the developers can explore and experiment with what's already available. The simulator on GitHub already has a bridge for ROS version 1 and ROS version 2. It also has examples of using popular ROS packages for navigation and manipulation through boxes nav2 and MoveIT. While many of these developers are already using Isaac Sim to generate synthetic data for training perception stacks in their robots.

This latest version of the Isaac Sim brings significant support for the ROS developers. Along with Nav2 and MoveIT support, the new Isaac Sim includes support for ROS in ROS April Tag, Stereo camera, TurtleBot3 Sample, ROS services, Native Python ROS support and usage, and even the ROS manipulation and camera sample.

This wide range of support will enable developers from different domains and fields to work efficiently in robotics. For example, developers will quickly work on domain-specific data from hospitals, agriculture, or stores. The resultant tools and support released from the Nvidia and Open Robotics partnership will enable developers to use these data and augment them in the real world for training robots. As Gopala Krishna put it, ”they can use that data, our tools and supplement that with real-world data to build robust, scalable models in photo-realistic environments that obey the laws of physics.” He claimed with the remark that Nvidia would also release pre-trained models.

On the remark about performance uplift in these perception stacks, Gopala Krishna said, “The amount of performance gain will vary depending on how much inherent parallelism exists in a given workload. But we can say that we see an order of magnitude increase in performance for perception and AI-related workloads.” Nvidia’s Gopala Krishna also remarked that the program would increase performance and much better power efficiency with appropriate processor use for an acceleration of different tasks.

Gopala Krishna also noted that Nvidia is working closely with Open Robotics to streamline the ROS framework for hard accelerations. The framework will also see multiple new releases of its hardware-accelerated software package, Isaac GEM. Some of these releases will focus on robotics perception, while further support for more sensors and hardware will arrive on the simulation technology side. The release will also contain samples that are relevant to the ROS community.

This development will aid the growing market of robotics. Especially after the COVID, the growth of the robotic market seems to skyrocket, with more and more industries and companies lining up to use and adopt robotics, from manufacturing and production lines to health care and agriculture usage.

Nvidia and Open Robotics partnership will see the advancement of AI and technologies like Machine Learning and Deep Learning at a rapid pace now with the support of NVIDIA hardware in robotics. Researchers estimate that the global robotics market will cross 210 Billion US Dollars. This estimate is likely to increase with the rapid development of AI and technologies like semiconductor technology, sensors, networking technology with 5G.

This collaboration between Nvidia and Open Robotics will only add valuation to this market with innovative platforms like Nvidia Isaac and ROC, helping developers develop more efficient, robust, and innovative robots and robotic applications.

It will also help the open-source community of robot development since this partnership brings together two of the most significant robotics development communities with ROC and Nvidia Isaac. Furthermore, FS Studio collaborates with this growing community to release its robotic simulation solution, ZeroSim, alongside the Nvidia and Open Robotics partnership. Thus, it will help the development bring together with collaboration and push the robotic development further. Now with the dawn of Industry 4.0, companies are moving towards digital technology. This movement can be seen with industries adopting digital solutions with robotics in different fields from production and manufacturing to the board paradigm of human-robot collaboration possibilities.

Robot programming software is a software solution that helps program or code a robot for its use or operation. Offline Robot Programming Software is also the same.

With the advancement of technology, Industry 4.0 is inching swiftly closer towards us faster than ever. Industry 4.0, also known as the Fourth Industrial Revolution, is the age of digitization where every industry has digital technology at its core. Consequently, digital technology is continuously evolving. Today, it almost seems inevitable for industries to adopt digital technology instead of relying on the traditional approach to industry, manufacturing, and product innovation.

Robotic technology is also continuously evolving, with robots today more capable than ever in various fields and fronts, even unseen in the last decade. Moreover, with the complexity and sophistication of the robots increasing, they are constantly getting more and more complex to program, code, and even develop.

However, with increasing complexity in technology, it is also getting more and more adaptable, usable, accessible, and easy to use. It’s because newer bleeding-edge technological solutions help keep these complex problems and technology operable and functional with great ease of use and access. One of the similar problems regarding the increasing complexity is currently running alongside the robotic industry.

The Robotics industry is far more complex, risky, and resource-hungry than most technological undertakings out there. Due to the growing industry use cases for robot and their ability to fulfill these use cases. The nature of robotic technology is that numerous parts and systems converge together to form a single system unit that can perform various tasks and operations using these parts and systems. Due to this nature, alongside the already complex building blocks of robots, i.e., the components and different systems, integrating these building blocks to work in an efficient cohesion with each other is a huge undertaking.

For easing the difficulty of integrating different parts and systems, many state-of-the-art industries and companies are starting to use robotic computer simulations. Simulations are a great innovation of digital technology that can help develop robotics through research, design, development, and production. Moreover, even after production, robotic software can now help robot operations, maintenance, and programming with different robot programming software solutions.

What is Offline Robot Programming Software?

Offline Robot Programming software is an “offline” approach to programming or coding a robot. This “offline” approach takes the usual method of programming a robot, i.e., teaching pendants away while doing the “teaching” part through the software remotely. However, this remote programming of the robot takes away the need of taking the physical robot out of production; instead can program and code robots virtually through software.

Teach pendants the most common interface to program an industrial robot. The device helps control an industrial robot remotely and teaches them to move or act in a certain way. For example, these devices can program or code the robots to follow a specific path or perform a certain action in a particular manner. These devices also allow the operator to control and work with these robots without being physically present or in tether connection with the robot. It means robot programmers or operators get to control the robots and “teach” them remotely. Technicians usually use these devices for testing or programming, or coding of industrial robots. Hence, teaching pendants are a crucial part of industrial robotics.

Offline Robot Programming Software replaces this teaching pendant with a more elegant and efficient solution with the power of software and simulation technology. Due to the control over robots with software since robotic OLP or Robotic Offline Programming allows for uploading programs and codes through software updates. Furthermore, software developers and robot operators can generate these programs and codes through robotic simulation software in a PC rather than using the robot physically. OLP is, therefore, a more elegant, efficient, and more modern way to program industrial robots.

Why is Offline Robot Programming Software Important?

Even though these pendants are helpful and crucial to industrial robotic operations, they remain one of the bottlenecks of industrial robotics. Right off the bat, these devices are very slow and time-consuming. It's also very resource-consuming and requires personal at all times to operate. Furthermore, pendants also require the presence of the actual robot. They need the robot to be physically active in the teaching mode rather than doing other work during the teaching process, which is usually very long.

Hence, during teaching, the robot cannot be in production or be doing other functional tasks teaching process is very lengthy and tediously for the more complex robots with various joints or movement points and axes. The robot programmer has to program multiple joints and parts to code the robot manually, which is very time-consuming. The programmer will also have to take out the production robot during and until the teaching process. It will surely hamper the production line, and hence downtimes become longer.

In any industrial setup, the production line is the most vital part of it. So much so that the whole manufacturing or production plant usually is based around it. Holding such importance, the optimization of production lines is a very crucial task in any industry. Downtimes, irregularities, or faults in the production lines and components around it means it directly hampers the sector. Moreover, machines like robots, especially the ones with automation, are very crucial in production lines. Hence, production lines must not stop nor deter it due to the robots.

However, with robotic OLP, industries can remove and eliminate all these disadvantages and bottlenecks from production. Instead of teaching these industrial robots online, offline programming eliminates the downtime for programming these robots completely. With this power in their hands, production lines can now completely get rid of time for programming. Instead, industries can use all these times in the actual production and get better returns.

With OLP, automation comes one step closer in production setups. Offline Robot Programming software enables rapid prototyping to test programs and codes before uploading them to the robots through simulation software. Furthermore, simulations are now very technologically smart such that they can simulate all robot parts, mechanics, systems, and movements. With such capability in hand, robot programming and even robot development and the building will become very easy. Due to this, testing, training, and evaluating robots virtually become very easy through OLP. Furthermore, it allows for error detection and verification of programs and robot capability to perform tasks and operations even before they are physically present.

Apart from this, Offline Robot Programming software also increases the productivity of production lines and robot operators and developers. Furthermore, OLP also provides greater profitability and has a better Return On Investment (ROI). Moreover, with OLP, one can test and prove new and better project or concept ideas in their quotation phase without investing in physical resources.

OLP allows for not only training and testing but also helps in maintenance and repairs too. OLP can help to track down potential faults and errors even before they occur or after they occur. It further makes the production efficiency and without any downtimes possibly in future too.

Not only is OLP advantageous and beneficial for regular industrial robots, but it's also essential and can be a boon for some industries that involve high risk. For example, industries like aviation, nuclear, automotive are very high-risk industries. Testing robots in these industries is a sensitive matter. Hence OLP is a requirement in these industries to train and test robots efficiently. Furthermore, without, OLP it is likely not even possible and feasible for industries like the space industry able to undertake projects and accomplish them.

Offline Robot Programming software is generally seen as a technology with high complexity and requiring very skillful personals. But that is not the case. Various companies like FS Studio provide solutions when it comes to offline robot programming. Companies like these can help industries get started with OLP and thrive on enabling substantial new possibilities and opportunities. With decades of experience and expertise in fields like Artificial Intelligence (AI), Virtual Reality (VR), Augmented Reality (AR), and Simulation technology, FS Studio, can provide companies with proper and efficient OLP solutions to propel their industries with more efficient, safe and effective production lines. With the advent of Industry 4.0 upon us, companies and industries now must look for better alternatives and modern approaches to the industry. Digitization of industries is the future where digital technology will be at the core of all industries with efficient and smart solutions. OLP with simulation technology enables rapid prototyping, testing, development, and superior research and development (R&D) along with faster and efficient programming or coding of industrial robots. Furthermore, industries can collaborate with different OLP providers to determine the best solution for their particular industry and production and help them integrate their existing robots and production for a more smooth transition towards Offline Robot Programming Software.