training a robot - 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.

AR and VR Technologies Guide Robots to improve their manufacturing process. From the assembly line to fieldwork, there is always a need for better technology that will help with efficiency and accuracy.

Augmented Reality (AR) and Virtual Reality (VR) technologies can provide some of these solutions. Industries like automotive, aerospace, medical device development, military training simulation, etc., have already used AR/VR technology.

Still, it can also be applied to robotics end-to-end design processes, including concept generation, prototyping, and testing on-site or remotely in immersive environments. Perhaps one of the best uses for AR and VR in robotics is to make training easier.

Here are some current and potential applications of AR and VR in robotics:

Augmented Reality to Improve Human-Robot Communication:

Robots have long been used to automate tasks in the workplace, but now they're evolving beyond just being mindless laborers.

As such, humans must interact with robots while performing various work-related activities and functions that were once left up exclusively between machines themselves.

This new trend has come about due essentially because these high tech devices are becoming more advanced than ever before - capable of doing things like learning on their own

The future of work is coming sooner than you think. With robots replacing humans in many tasks, it will be necessary for the two groups (robotics specialists and humanity) to find common ground to communicate with each other effectively.

At the University Of Boulder Colorado's robotics lab, researchers have found an innovative way by implementing augmented reality technology into drones to allow users to figure out how robots and humans could communicate in different targeted working spaces.

The researchers concluded that they could improve human-robot interaction in co-working spaces significantly by using AR & VR.

Virtual Reality Trains Robots for Object Identification:

A robot that can learn and predict behavior on its own through data exposure is an exciting idea in artificial intelligence.

A robot's learning process is not limited to humans. It can also take in data and learn how to group it with similar categories, discriminate between different items or recognize new ones that look like those the bot has been exposed to before.

Researchers at the University of California, Berkeley succeeded in training a robot to pick up objects indicated by them after being introduced with different items through virtual reality.

One example shows how robots are becoming more advanced and can assist people who need them around their office space or factories.

Using VR, robots can be taught anything with the least amount of cost and effort. In addition, it means that trainers only need a 3D model for their virtual Reality training sessions - not real-life models!

That's an incredible convenience because it allows them to explore large-scale territories without physical constraints on time or space like in traditional fieldwork situations.

Medical Robots Equipped with AR:

Robotic surgery is already being performed in hospitals worldwide, but there's more to it than meets the eye. For one thing, advanced robotic arms can be found performing delicate surgeries or assisting with other medical tasks like drug manufacturing facilities.

These machines can provide direct assistance while ensuring safety during an operation by monitoring vital signs remotely and autonomously navigating through obstacles independently.

The students at Monterrey Institute of Technology and Higher Education in Mexico created a robotic exoskeleton that can assist people with mobility problems to stand and move around the house or office freely without fear that they will fall on their own.

This outer body was equipped with augmented reality capacities so its human operator could view each part while deciding where it would best fit for optimal use!

Programming by Demonstration Taken to the Next Level:

Robots need to be programmed with complex tasks such as dangerous ones before they can perform these actions. So, robots are programmed by demonstration.

Programming by demonstration is similar to employee training: the human operator demonstrates a task until it can replicate it on its own or teach an existing robot how to do something new.

However, this isn't always possible since there are some things even machines cannot learn without being shown first-hand through direct instruction from someone who knows what needs doing best!

AR and VR technologies allow designers to create the entire demonstration in a virtual environment or superimposed over real life.

For example, OpenAI robots developed by Elon Musk's company trained robots using Vision Network. This technique is effective with swarm robotics too!

Robotics and AR technology in Manufacturing:

The use of robotics and AR technology will have a significant impact on Manufacturing. It allows manufacturers to show their design, building site, or product faster without much hassle.

VR comes as a standard preparation method for people who face physical hazards, such as 3D printing models or simulations that can reduce the need for costly modeling equipment and improve product quality in the workplace.

AR & VR models in Urban planning:

With new construction projects becoming more popular, developers need to understand how these elements will impact current urban environments.

So, they can maximize profit over all-time horizons by generating AR (Augmented Reality) and VR-modeled before building anything at all.

Integrating virtual reality and robotics for construction employees can have immense benefits. The idea behind this is to allow humans more creative space while robots take care of repetitive tasks.

In this way, humans can focus more on the implementation of strategies for better outcomes.

Use of virtual reality in military training programs:

The use of virtual reality in military training programs can help soldiers understand how to use military robots and drones.

For this purpose, organizations create a virtual battlefield that allows military officers to test different tactics on an interactive landscape with realistic visuals for each drone's move.

For example, the military trainer can turn his head left or right or move it forward or change the altitude levels on one's drone.

With this approach, soldiers can learn to avoid various obstacles and guide a drone through the battlefield.

Virtual reality also helps them to teach how they might tackle situations on an actual field of battle or attack enemy bases at just about any given moment with their robots in tow!

Robotics & VR work hand-in-hand so that we may develop more advanced training programs for our warriors--both human beings and bots alike.

AR and VR in Crime Department:

AR and VR are changing the way that we think about crime scenes.

Forensic experts can use these technologies to label evidence at a time digitally. For example, AR annotation allows forensic investigators to digitally label proof traces at crime scenes and update, exchange, and transfer lists of evidence.

On the other hand, VR technology maps training goals explicitly and develops & tests a virtual prototype to train agents to adapt to challenging work circumstances.

VR in Teleoperating Robots:

Virtual reality is being used to design teleoperating robots that can eliminate dangerous factory work environments.

This type of robot involves replicating what we do with our hands; they can be controlled remotely and act accordingly with the help of hand controllers and multiple display sensors.

For instance, FS Studio has built VR Teleoperation for Robotics for a major car company. We developed an immersive virtual reality environment for the client's training and remote operation of the robotic system. We even integrated two types of haptic controllers in the process!

We successfully created a Unity 3D environment that would import robots using URDF files. The resulting VR environment had terrific functions.

For example, it could control the robot, display the data and video from the various sensors and cameras, determine path and route navigation, and show all of this in an immersive 3D VR environment.

The future of robotics is here, and it has never been brighter. Thanks to the advancements in new technologies like AR and VR, we will soon see robots that can think for themselves and work with humans as partners.

We've given you a quick overview of these cutting-edge innovations but know there is so much more out there just waiting to be explored!