The Three Computer Solution: Driving the Upcoming AI Robotics Revolution.
Training, simulation, and inference are being used to speed up industrial, Physical AI-based systems, ranging from factories to humanoids.
For generative AI, ChatGPT was the big bang moment. Almost every inquiry may have an answer produced, revolutionizing digital work for knowledge workers in areas including software development, customer support, content production, and company management.
Artificial intelligence in the form of physical AI, which is found in factories, humanoids, and other industrial systems, has not yet reached a breakthrough.
This has slowed down sectors including manufacturing, logistics, robotics, and transportation and mobility. However, three computers that combine sophisticated training, simulation, and inference are about to alter that.
The Development of Multimodal, Physical AI
For sixty years, human programmers’ “Software 1.0” serial code operated on CPU-powered general-purpose computers.
Geoffrey Hinton and Ilya Sutskever helped Alex Krizhevsky win the 2012 ImageNet computer image identification competition using AlexNet, a pioneering deep learning model for picture categorization.
This was the first industrial AI usage. The advent of GPU-based machine learning neural networks sparked the Software 2.0 era.
Software now creates software. Moore’s law is being left far behind as the world’s computing workloads move from general-purpose computing on CPUs to accelerated computing on GPUs.
Diffusion and multimodal transformer models have been taught to provide responses using generative AI.
The next token in modes like letters or words may be predicted using large language models, which are one-dimensional. Two-dimensional models that can anticipate the next pixel are used to generate images and videos.
The three-dimensional reality is beyond the comprehension and interpretation of these models. Physical AI then enters the picture.
With generative AI, physical AI models are able to see, comprehend, engage with, and traverse the physical environment. The utility of physical AI via robotics is becoming more widely recognized because to advancements in multimodal physical AI, faster computation, and large-scale physically based simulations.
Any system that has the ability to see, think, plan, act, and learn is called a robot. Many people think of robots as humanoids, manipulator arms, or autonomous mobile robots (AMRs). However, there are several more kinds of robotic embodiments.
Autonomous robotic systems will soon be used for anything that moves or that keeps an eye on moving objects. These devices will be able to perceive their surroundings and react accordingly.
Physical AI will replace static, humanly run systems in a variety of settings, including data centers, factories, operating rooms, traffic management systems, and even whole smart cities.
Humanoids and Robots: The Next Frontier
Because they can function well in settings designed for people and need little modification for deployment and operation, humanoid robots are the perfect example of a general-purpose robotic manifestation.
Goldman Sachs estimates that the worldwide market for humanoid robots would grow to $38 billion by 2035, more than six times the $6 billion predicted for the same period only two years ago.
Globally, scientists and engineers are vying to create this next generation of robots.
Three Computers for Physical AI Development
Three accelerated computer systems are needed to manage physical AI and robot training, simulation, and runtime in order to create humanoid robots. The development of humanoid robots is being accelerated by two developments in computing: scalable, physically based simulations of robots and their environments, as well as multimodal foundation models.
Robots now have 3D vision, control, skill planning, and intelligence with to advancements in generative AI. Developers may hone, test, and perfect robot abilities in a virtual environment that replicates the laws of physics via large-scale robot simulation, which lowers the cost of real-world data collecting and guarantees that the robots can operate in safe, regulated environments.
To help developers build physical AI, NVIDIA has constructed three processors and faster development platforms.
First, models are trained on a supercomputer:NVIDIA NeMo on the NVIDIA DGX platform allows developers to train and optimize robust foundation and generative AI models. Additionally, they may use NVIDIA Project GR00T, which aims to create general-purpose foundation models for humanoid robots so that they can mimic human gestures and comprehend spoken language.
Second: using application programming interfaces and frameworks such as NVIDIA Isaac Sim, NVIDIA Omniverse, which runs on NVIDIA OVX servers, offers the simulation environment and development platform for testing and refining physical AI.
Developers may create vast quantities of physically based synthetic data to bootstrap robot model training, or they can utilize Isaac Sim to simulate and test robot models. To speed up robot policy training and improvement, researchers and developers may also use NVIDIA Isaac Lab, an open-source robot learning framework that underpins robot imitation learning and reinforcement learning.
Finally, a runtime computer receives taught AI models: For small, on-board computing requirements, NVIDIA created the Jetson Thor robotics processors. The robot brain is a collection of models that are installed on a power-efficient on-board edge computing system. These models include control policy, vision, and language models.
Robot manufacturers and foundation model developers may employ as many of the accelerated computing platforms and systems as necessary, depending on their workflows and areas of complexity.
Constructing the Upcoming Generation of Self-Sustained Facilities
All of these technologies come together to create robotic facilities.
Teams of autonomous robots may be coordinated by manufacturers like Foxconn or logistics firms like Amazon Robotics to assist human workers and keep an eye on manufacturing operations using hundreds or thousands of sensors.
Digital twins will be used in these self-sufficient industries, plants, and warehouses. The digital twins are used for operations simulation, layout design and optimization, and above all software-in-the-loop testing for robot fleets.
“Mega,” a factory digital twin blueprint built on Omniverse, allows businesses to test and improve their fleets of robots in a virtual environment before deploying them to actual plants. This promotes minimum disturbance, excellent performance, and smooth integration.
Mega enables developers to add virtual robots and their AI models the robots’ brains to their manufacturing digital twins. In the digital twin, robots carry out tasks by sensing their surroundings, using reasoning, deciding how to go next, and then carrying out the planned activities.
The world simulator in Omniverse simulates these activities in the digital environment, and Omniverse sensor simulation allows the robot brains to observe the outcomes.
While Mega painstakingly monitors the condition and location of each component inside the manufacturing digital twin, the robot brains use sensor simulations to determine the next course of action, and the cycle repeats.
Within the secure environment of the Omniverse digital twin, industrial firms may simulate and verify modifications using this sophisticated software-in-the-loop testing process. This helps them anticipate and mitigate possible difficulties to lower risk and costs during real-world deployment.
Using NVIDIA Technology to Empower the Developer Ecosystem With three computers, NVIDIA speeds up the work of the worldwide robotics development and robot foundation model building ecosystem.
Empowering the Developer Ecosystem With NVIDIA Technology
In order to create UR AI Accelerator, a ready-to-use hardware and software toolkit that helps cobot developers create applications, speed up development, and shorten the time to market of AI products, Universal Robots, a Teradyne Robotics company, used NVIDIA Isaac Manipulator, Isaac accelerated libraries, and AI models, as well as NVIDIA Jetson Orin.
The NVIDIA Isaac Perceptor was used by RGo Robotics to assist its wheel. Because AMRs have human-like vision and visual-spatial knowledge, they can operate anywhere, at any time, and make wise judgments.
NVIDIA’s robotics development platform is being used by humanoid robot manufacturers such as 1X Technologies, Agility Robotics, Apptronik, Boston Dynamics, Fourier, Galbot, Mentee, Sanctuary AI, Unitree Robotics, and XPENG Robotics.
Boston Dynamics is working with Isaac Sim and Isaac Lab to develop humanoid robots and quadrupeds to increase human productivity, address labor shortages, and put warehouse safety first.
Fourier is using Isaac Sim to teach humanoid robots to work in industries including manufacturing, healthcare, and scientific research that need a high degree of interaction and flexibility.
Galbot pioneered the creation of a simulation environment for assessing dexterous grasping models and a large-scale robotic dexterous grasp dataset called DexGraspNet that can be used to various dexterous robotic hands using Isaac Lab and Isaac Sim.
Using the Isaac platform and Isaac Lab, Field AI created risk-bounded multitask and multifunctional foundation models that allow robots to work safely in outside field conditions.
The physical AI age has arrived and is revolutionizing robotics and heavy industries worldwide.
