Officials from nine countries, including the United States, France, Poland, the United Arab Emirates and Morocco, presented national approaches to bringing technology into education systems, underscoring how AI policy is increasingly moving from research labs into classrooms and public-sector strategy. The White House said the summit brought together policymakers and private-sector leaders as governments treat AI not only as a learning tool, but also as an economic and geopolitical priority.

In her keynote, Melania Trump outlined what she called three forces likely to shape the next generation: AI-driven personalized learning, the emergence of humanoid educators for at-home use, and the broader role of technology and education in economic growth.

“The future of AI is ‘personified’ – it will be formed in the shape of humans,”

Melania Trump said.

Very soon, artificial intelligence will move from our mobile phones to humanoids that deliver utility.”

Melania Trump also used the summit to introduce what the White House described as an American-built humanoid system, Figure 3, calling it “my first American-made humanoid guest in the White House.” The administration said the appearance marked the first formal presentation of that kind of technology to international leaders in a diplomatic setting at the White House, turning the summit into a demonstration of how embodied AI may become part of future education, home assistance and public-facing services.

The First Lady framed that future in distinctly consumer-tech terms. She asked attendees to “imagine a humanoid educator named ‘Plato,’” describing an always-available AI system able to adapt lessons in real time to a student’s pace, prior knowledge and even emotional state.

“Plato will provide a personalized experience, adaptive to the needs of each student. Plato is always patient, and always available,”

Melania Trump said, while adding that

“we must balance our tech optimism with caution. The safety of our next generation is always paramount.”

Melania Trump also made a broader industrial argument, urging closer coordination between government and the private sector. Referring to the State Department session, she highlighted participation from companies including Meta, Microsoft, OpenAI, X, Palantir, Google, Zoom and Adobe, and said,

“We can accelerate civilization’s march forward when enterprise delivers innovation, government creates scale, and our capital markets finance the distribution of these emerging technologies.”

The summit fits into a larger White House push around AI-enabled education. The coalition’s stated goal is to help children learn, grow and thrive through the safe and innovative use of advanced technology, while expanding access to tools and pairing those deployments with digital safety measures.

At its core, the event was both a diplomatic exercise and a signal about where parts of the policy conversation around AI are heading next: away from abstract debate over models alone, and toward real-world systems that mix software, hardware, education and national competitiveness.

“We stand at a turning point because of artificial intelligence – The Age of Imagination,”

Melania Trump said.

“This technology may reset the modern world order and rebalance power.”

Image: Official video posted on the White House website (screenshot)

He stated he’s pursuing this project because semiconductor manufacturers are not producing chips fast enough to meet his companies’ AI and robotics demands. Musk said:

“We either build the Terafab or we don’t have the chips, and we need the chips, so we build the Terafab.”

According to Bloomberg Musk shared his plans during an event in downtown Austin, Texas, with a photo indicating that the “Terafab” facility will be “gigafactory,” located near Tesla’s Austin headquarters.

He also added that the aim is to produce chips capable of supporting 100–200 gigawatts of computing power annually on Earth, as well as one terawatt in space. He did not provide a timeline for the plan.

Image: Presentation of the Terafab project

Material by Yana Petrova

Photo: Hyundai Motor Group

At its Delivering the Future event in Dortmund, Germany, Amazon announced the debut of Vulcan, a groundbreaking robot that introduces a fundamental new capability to warehouse automation: the sense of touch.

Unlike previous robotic systems that rely primarily on vision and pre-programmed motions, Vulcan is engineered to physically “feel” the objects it handles—offering a major leap forward in dexterity, safety, and efficiency across Amazon’s vast network of fulfillment centers.

“Vulcan represents a fundamental leap forward in robotics,”

said Aaron Parness, Amazon’s Director of Applied Science.

“It’s not just seeing the world, it’s feeling it—enabling capabilities that were impossible for Amazon robots until now.”

The announcement was made via Amazon’s official blog post and showcased how Vulcan is already transforming warehouse operations in locations such as Spokane, Washington, and Hamburg, Germany.

From “Numb and Dumb” to Tactile Intelligence

While robots have become adept at tasks ranging from autonomous driving to cleaning pet hair, most commercial units lack a sense of touch—rendering them fragile or clumsy in environments that require nuance.

“In the past, when industrial robots have unexpected contact, they either emergency stop or smash through that contact,”

said Parness.

“They often don’t even know they have hit something.”

This is the problem Vulcan was built to solve.

Equipped with advanced force-feedback sensors and an “end of arm tooling” system that mimics human-like grip adjustments, Vulcan can manipulate items with care—gently repositioning objects inside densely packed bins without causing damage.

Its design resembles a ruler attached to a hair straightener, where:

• The ruler component makes space in crowded bins,
• The paddles grip and insert items using built-in conveyor belts that help “zhoop” them into place.

Vulcan’s ability to pick and stow items makes our associates’ jobs easier—and our operations more efficient.

Built for the Bin: Solving Amazon’s Unique Storage Challenge

In Amazon’s warehouses, items are stored in fabric-covered pods split into one-foot square compartments—each containing up to ten items. The irregularity and density of these compartments have long posed a challenge to robotic systems.

While earlier robots like Sparrow, Cardinal, and Robin relied on computer vision and suction cups to handle packages, they lacked the tactile intelligence to finesse objects in tight spaces. Vulcan, however, changes the game.

Using a suction-based picking arm guided by a camera and stereo vision system, Vulcan identifies items and the best gripping points while avoiding accidental co-extraction of surrounding objects—an error engineers refer to as “co-extracting non-target items.”

Vulcan can pick and stow about 75% of the diverse inventory found in fulfillment centers, performing at speeds that rival Amazon’s human workers. And when it encounters an object it can’t confidently handle, it’s smart enough to call in a human colleague—striking a balance between AI autonomy and human judgment.

Vulcan uses an arm that carries a camera and a suction cup to pick items from our storage pods.

Enhancing Safety and Ergonomics for Employees

One of Vulcan’s key contributions is in improving worker safety and reducing ergonomic strain.

Traditionally, reaching items stored in the top or bottom rows of pods—some as high as eight feet—required workers to use ladders or stoop to floor level. Vulcan now handles these less ergonomic zones, allowing employees to work comfortably at waist height.

“Working alongside Vulcan, we can pick and stow with greater ease,”

said Kari Freitas Hardy, a front-line employee at Amazon’s Spokane facility.

“It’s great to see how many of my co-workers have gained new job skills and taken on more technical roles.”

The company has already deployed over 750,000 robots across its operations, including systems like Proteus, Titan, and Hercules, all built to handle physically demanding tasks. Vulcan is the latest and most advanced addition in this line—focusing on precision and adaptability rather than brute strength.

Vulcan will let our associates spend less time on step ladders and more time working in their power zone.

A Decade of Robotics Innovation

Amazon’s approach to robotics has never been about building flashy tech for its own sake. Instead, the company zeroes in on specific operational problems and builds purpose-driven solutions.

“We pick out important problems and find or develop solutions—we don’t create interesting tech and then look for ways to use it,”

Parness emphasized.

Vulcan’s development began with a simple observation: each time a worker uses a ladder to access a high shelf, efficiency drops and injury risk increases. Tackling this required breakthroughs in physical AI, including:

• Real-world training based on tactile feedback rather than simulation,
• Algorithms to identify item types and bin availability,
• Adaptive grip mechanics to handle everything from tubes of toothpaste to delicate electronics.

Vulcan was trained on thousands of real-world scenarios, and like a child learning through experience, it improves its understanding of object properties through trial and error.

“This is a technology that three years ago seemed impossible,”

Parness said,

“but is now set to help transform our operations.”

Vulcan represents “a technology that three years ago seemed impossible but is now set to help transform our operations,” says Aaron Parness, Amazon’s director of robotics AI.

Empowering the Workforce of the Future

The ripple effects of Vulcan extend beyond automation. As robots take on more of the physical burden, Amazon is investing in reskilling its workforce through programs like Career Choice, helping employees transition into roles like robotics maintenance and automation systems engineering.

With robots now assisting in 75% of customer orders, Amazon’s strategy appears to be less about replacing humans and more about augmenting them—with Vulcan standing as the latest proof that automation and human labor can coexist and even elevate each other.

Images/Photos: Amazon

NVIDIA has unveiled a suite of cutting-edge technologies to accelerate humanoid robot development, featuring NVIDIA Isaac GR00T N1—the world’s first open and fully customizable foundation model for advanced humanoid reasoning and skills.

The other technologies include simulation frameworks and blueprints such as the NVIDIA Isaac GR00T Blueprint for generating synthetic data, as well as Newton, an open-source physics engine — under development with Google DeepMind and Disney Research — purpose-built for developing robots.

Available now, GR00T N1 is the first of a family of fully customizable models that NVIDIA will pretrain and release to worldwide robotics developers — accelerating the transformation of industries challenged by global labor shortages estimated at more than 50 million people.

“The age of generalist robotics is here,”

said Jensen Huang, founder and CEO of NVIDIA.

“With NVIDIA Isaac GR00T N1 and new data-generation and robot-learning frameworks, robotics developers everywhere will open the next frontier in the age of AI.”

GR00T N1 Advances Humanoid Developer Community

The GR00T N1 foundation model features a dual-system architecture, inspired by principles of human cognition.

• “System 1” is a fast-thinking action model, mirroring human reflexes or intuition.
• “System 2” is a slow-thinking model for deliberate, methodical decision-making.

Powered by a vision language model, System 2 reasons about its environment and the instructions it has received to plan actions. System 1 then translates these plans into precise, continuous robot movements. System 1 is trained on human demonstration data and a massive amount of synthetic data generated by the NVIDIA Omniverse™ platform.

GR00T N1 can easily generalize across common tasks — such as grasping, moving objects with one or both arms, and transferring items from one arm to another — or perform multistep tasks that require long context and combinations of general skills. These capabilities can be applied across use cases such as material handling, packaging and inspection.

Developers and researchers can post-train GR00T N1 with real or synthetic data for their specific humanoid robot or task.

In his GTC keynote, Huang demonstrated 1X’s humanoid robot autonomously performing domestic tidying tasks using a post-trained policy built on GR00T N1. The robot’s autonomous capabilities are the result of an AI training collaboration between 1X and NVIDIA.

“The future of humanoids is about adaptability and learning,”

said Bernt Børnich, CEO of 1X Technologies.

“While we develop our own models, NVIDIA’s GR00T N1 provides a significant boost to robot reasoning and skills. With minimal post-training data, we fully deployed on NEO Gamma — advancing our mission of creating robots that are not just tools, but companions capable of assisting humans in meaningful, immeasurable ways.”

Among the additional leading humanoid developers worldwide with early access to GR00T N1 are Agility Robotics, Boston Dynamics, Mentee Robotics and NEURA Robotics.

NVIDIA, Google DeepMind and Disney Research Focus on Physics

NVIDIA announced a collaboration with Google DeepMind and Disney Research to develop Newton, an open-source physics engine that lets robots learn how to handle complex tasks with greater precision.

Built on the NVIDIA Warp framework, Newton will be optimized for robot learning and compatible with simulation frameworks such as Google DeepMind’s MuJoCo and NVIDIA Isaac™ Lab. Additionally, the three companies plan to enable Newton to use Disney’s physics engine.

Google DeepMind and NVIDIA are collaborating to develop MuJoCo-Warp, which is expected to accelerate robotics machine learning workloads by more than 70x and will be available to developers through Google DeepMind’s MJX open-source library, as well as through Newton.

Disney Research will be one of the first to use Newton to advance its robotic character platform that powers next-generation entertainment robots, such as the expressive Star Wars-inspired BDX droids that joined Huang on stage during his GTC keynote.

“The BDX droids are just the beginning. We’re committed to bringing more characters to life in ways the world hasn’t seen before, and this collaboration with Disney Research, NVIDIA and Google DeepMind is a key part of that vision,”

said Kyle Laughlin, senior vice president at Walt Disney Imagineering Research & Development.

“This collaboration will allow us to create a new generation of robotic characters that are more expressive and engaging than ever before — and connect with our guests in ways that only Disney can.”

NVIDIA and Disney Research, along with Intrinsic, announced an additional collaboration to build OpenUSD pipelines and best practices for robotics data workflows.

More Data to Advance Robotics Post-Training

Large, diverse, high-quality datasets are critical for robot development but costly to capture. For humanoids, real-world human demonstration data is limited by a person’s 24-hour day.

Announced today, the NVIDIA Isaac GR00T Blueprint for synthetic manipulation motion generation helps address this challenge. Built on Omniverse and NVIDIA Cosmos Transfer world foundation models, the blueprint lets developers generate exponentially large amounts of synthetic motion data for manipulation tasks from a small number of human demonstrations.

Using the first components available for the blueprint, NVIDIA generated 780,000 synthetic trajectories — the equivalent of 6,500 hours, or nine continuous months, of human demonstration data — in just 11 hours. Then, combining the synthetic data with real data, NVIDIA improved GR00T N1’s performance by 40%, compared with using only real data.

To further equip the developer community with valuable training data, NVIDIA is releasing the GR00T N1 dataset as part of a larger open-source physical AI dataset — also announced at GTC and now available on Hugging Face.

NVIDIA GR00T N1 training data and task evaluation scenarios are now available for download from Hugging Face and GitHub. The NVIDIA Isaac GR00T Blueprint for synthetic manipulation motion generation is also now available as an interactive demo on build.nvidia.com or to download from GitHub.

The NVIDIA DGX Spark personal AI supercomputer, also announced at GTC, provides developers a turnkey system to expand GR00T N1’s capabilities for new robots, tasks and environments without extensive custom programming.

The Newton physics engine is expected to be available later this year.

Researchers in China are helping 17 million blind people by creating guide dogs controlled by artificial intelligence, GIZMOCHINA shares. The project, developed by scientists at the Polytechnic University of China, provides functions such as accompanying and navigation.

The guide dog, controlled by artificial intelligence, is trained to understand the commands given to it. The attendant can also conduct conversations. It facilitates the daily life of visually impaired people by guiding them through the streets, helping them get into elevators or enter different buildings.

“Smart guide dogs using the language model can offer them more convenient and safe navigation, effectively improving their quality of life,” said Sun Zhe, an associate professor at the university.

Compared to the regular guide dogs we are used to, training AI guides is much cheaper. In addition, AI guide dogs can be created faster than regular dogs.

The project to create AI companions is still in the early stages of development, but according to China, it is possible that AI guide dogs will become widely available very soon.

The collaboration between the Polytechnic University of China and China Telecom Artificial Intelligence Research Institute accelerates the implementation of the project aimed at supporting the daily life of visually impaired people.

Humanoid robot company 1X Technologies, based in Mountain View, California, has raised $100 million in Series B funding with participation from Swedish venture capital firm EQT Ventures.

The company was able to raise more than $125 million in less than a year after raising $23.5 million in Series A2 funding from OpenAI and Tiger Global in early 2023.

This funding will support the production of safe and advanced androids at commercial scale. 1X Technologies’ biggest goal to date is to meet global employee needs.

The funds will also support 1X’s corporate customers in logistics and security.

NEO – the new home assistant

Meet NEO

The humanoid robot company has intentions to use the new capital, for its second generation Android NEO. Designed as a bipedal humanoid, NEO has a mission to help with everyday domestic activities.

The company asserts that what distinguishes NEO from other humanoid robots is its gentle and inherently secure design. In contrast to industrial machines, NEO is devoid of pinch-points or potential hazards, aligning with our dedication to providing a secure and user-friendly Android for consumers.

This focus on safety is crucial in bringing our vision to fruition, aiming to introduce a dependable and practical android into everyday life.

NEO’s soft construction resembling human features makes it safer and more comfortable than other robots. With a head, arms and legs similar to our own, NEO interacts with the world in a familiar way, performing activities such as walking, grasping objects and expressing emotions through facial cues.

Although NEO finds application in various sectors such as security, logistics, manufacturing, machine control and complex task management, its overall vision extends to becoming a valuable home assistant.

Envisioned for a long-term role in performing activities such as cleaning and organizing, NEO aims to integrate seamlessly into home life, increasing convenience and support for users.

While the company’s EVE android is currently working in logistics and guarding serving the enterprise market, NEO will be introduced into domestic environments, entering the household market first. It is on a fast track to market release and will be launched and available for consumers to pre-order within a short time.

At the beginning of the week, we chose to share with you a curious conversation between TechCrunch and Matthew Johnson-Roberson, in which he shares more about Robotics, its future, the role of Artificial Intelligence and other interesting issues related to Robotics.

Matthew Johnson-Roberson is an American researcher, entrepreneur and educator. As of January 2022, he is the director of the Robotics Institute at Carnegie Mellon University. Prior to that, he was a professor at the University of Michigan’s College of Engineering since 2013, where he co-directed UM’s Ford Center for Autonomous Vehicles (FCAV) with Ram Vasudevan.

His research focuses on computer vision and artificial intelligence, with specific applications to autonomous underwater vehicles and self-driving cars. He is also the co-founder and CTO of Refraction AI, a company focused on providing last-mile autonomous delivery.

What role(s) will generative AI play in the future of robotics?

Generative AI, through its ability to generate novel data and solutions, will significantly bolster the capabilities of robots. It could enable them to better generalize across a wide range of tasks, enhance their adaptability to new environments, and improve their ability to autonomously learn and evolve.

What are your thoughts on the humanoid form factor?

The humanoid form factor is a really complex engineering and design challenge. The desire to mimic human movement and interaction creates a high bar for actuators and control systems. It also presents unique challenges in terms of balance and coordination. Despite these challenges, the humanoid form has the potential to be extremely versatile and intuitively usable in a variety of social and practical contexts, mirroring the natural human interface and interaction. But we probably will see other platforms succeed before these.

Following manufacturing and warehouses, what is the next major category for robotics?

Beyond manufacturing and warehousing, the agricultural sector presents a huge opportunity for robotics to tackle challenges of labor shortage, efficiency, and sustainability. Transportation and last-mile delivery are other arenas where robotics can drive efficiency, reduce costs, and improve service levels. These domains will likely see accelerated adoption of robotic solutions as the technologies mature and as regulatory frameworks evolve to support wider deployment.

How far out are true general-purpose robots?

The advent of true general-purpose robots, capable of performing a wide range of tasks across different environments, may still be a distant reality. It requires breakthroughs in multiple fields including AI, machine learning, materials science, and control systems. The journey toward achieving such versatility is a step-by-step process where robots will gradually evolve from being task-specific to being more multi-functional and eventually general purpose.

Will home robots (beyond vacuums) take off in the next decade?

The next decade might witness the emergence of home robots in specific niches, such as eldercare or home security. However, the vision of having a general-purpose domestic robot that can autonomously perform a variety of household tasks is likely further off. The challenges are not just technological but also include aspects like affordability, user acceptance, and ethical considerations.

What important robotics story/trend isn’t getting enough coverage?

Despite significant advancements in certain niche areas and successful robotic implementations in specific industries, these stories often get overshadowed by the allure of more futuristic or general-purpose robotic narratives. The incremental but impactful successes in sectors like agriculture, healthcare, or specialized industrial applications deserve more spotlight as they represent the real, tangible progress in the field of robotics.

The Nature’s Communications Biology journal described the system and a set of experiments. The main aim of the research was to assist paralyzed patients by developing robots that can be controlled using a BCI, explains Anthony Alford, a Development Group Manager at Genesys Cloud Services, in an InfoQ article.

The robot’s software includes a semi-autonomous obstacle avoidance routinе with parameters that are updated using IRL based on error-related potentials. Aude Billard,  a lead researcher, commented:

“Assistance from robots could help [people with a spinal cord injury] recover some of their lost dexterity, since the robot can execute tasks in their place.”

BCI devices typically measure neural activity using internal implants or external sensors such as EEG electrodes. The main goal is to convert this sensor data into a signal that can be used as a computer input. Because directly commanding a robot manipulator via a BCI could be time-consuming and fatiguing, the team chose to investigate how a BCI could be used to adjust the behavior of a semi-autonomous robot manipulator.

Because of that the system adjusts the robot’s obstacle avoidance algorithm in response to the user’s error-related potentials (ErrP). To make this adjustment, the researchers implemented an IRL training algorithm. The algorithm learns both the reward function and the optimal action from a set of demonstrations. As the manipulator approached the obstacle, the robot would attempt to avoid the obstacle; if the user anticipated that the robot would not avoid the obstacle, the ErrP signal detected by the BCI was used to adjust the reward function and obstacle avoidance parameters.

In a set of experiments, the researchers found that their system could identify a user’s reward function in as few as three demonstrations. They also added that the approach was “robust to the natural variability and sub-optimal performance of the ErrP decoder,” a useful property, because the EEG sensing can be noisy.

However, machine learning, like every successful thing in this world, has its disadvantages. One of them is consuming too much human brain mimicry.

As we know, there are neurons in our brain that communicate with one another through so-called synapses. They are strengthened each time information flows through them. It is this plasticity that ensures that humans remember and learn, and researchers find inspiration in it in order to create a more efficient machine.  Imke Krauhausen, Ph.D. student at the Department of Mechanical Engineering at TU/e, explains:

“In our research, we have taken this model to develop a robot that is able to learn to move through a labyrinth. Just as a synapse in a mouse brain is strengthened each time it takes the correct turn in a psychologist’s maze, our device is ‘tuned’ by applying a certain amount of electricity. By tuning the resistance in the device, you change the voltage that controls the motors. They, in turn, determine whether the robot turns right or left.”

Krauhausen and her team created a robot with the help of a robotics kit, made by Lego. It is a Mindstorms EV3 and it is equipped with two wheels, traditional guiding software which is supposed to make sure it can follow a line, and a number of reflectance and touch sensors.

When put in a maze, the robot is told to turn to either return or to turn left every time it reaches a dead-end or diverges from the designated path to the exit. Krauhausen says:

“In the end, it took our robot 16 runs to find the exit successfully. And, what’s more, once it has learned to navigate this specific route (target path 1), it can navigate any other path that it is given in one go (target path 2). So, the knowledge it has acquired is generalizable.”

Organic material is used for the neuromorphic robot. It is both stable and able to maintain a large part of the specific states in which it has been tuned during the various runs through the labyrinth. This ensures that the learned behavior ‘sticks’, just like neurons and synapses in a human brain remember events or actions.

During the research, which dated from 2015 and 2017, the material was proved to be able to tune in a much larger range of conduction than inorganic and materials, and that it is able to ‘remember’ or store learned states for extended periods. Since then, organic devices have become a hot topic in the field of hardware-based artificial neural networks. Krauhausen added:

“Because of their organic nature, these smart devices can in principle be integrated with actual nerve cells. Say you lost your arm during an injury. Then you could potentially use these devices to link your body to a bionic hand,” says Krauhausen.

She also said that their robots rely on traditional software to move around. She admitted that she will be working on developing in the next phase of her research.