As with every major industrial revolution, the companies assembling the final products may not be the only ones to benefit. The real winners could be the manufacturers of the essential technologies required by every robot, regardless of its manufacturer, design or purpose.

This analysis is also at the heart of a recent Morgan Stanley report, which focuses less on robot brands and more on the companies capable of supplying the critical components that could equip millions of machines over the coming decades.

A Lesson from Industrial History

Economic history shows that technological revolutions often benefit infrastructure suppliers.

During the gold rush, sellers of shovels and pickaxes sometimes generated more wealth than the gold prospectors themselves.

More recently, the artificial intelligence boom has elevated companies such as NVIDIA, TSMC and ASML, which have become indispensable links in the global technology ecosystem.

Robotics could follow a similar path.

Behind every humanoid robot lies a network
of suppliers on which the entire robotics
revolution depends.

Bearings: An Invisible but Essential Component

Among these components, precision bearings occupy a strategic position.

Every joint, electric motor and rotating mechanism in a robot requires a system capable of reducing friction while ensuring precision, durability and a long operational lifespan.

A drone already contains several bearings.

An industrial robot may use several dozen.

An advanced humanoid robot can incorporate more than 70 bearings, distributed across its shoulders, elbows, wrists, hips, knees, ankles and even the actuators in its hands.

The more dexterous robots become, the greater their need for high-precision mechanical components.

Components: The Backbone of Robotics

Bearings, however, represent only one part of the equation.

Modern robotics relies on a range of technologies, none of which can be easily replaced:

• Precision bearings
• Harmonic drives
• Electromechanical actuators
• Torque motors
• Ball screws
• Absolute encoders
• Force and torque sensors
• Industrial cameras
• Specialised semiconductors for embedded AI

These components form the technological foundation on which future generations of industrial, medical, logistics and humanoid robots will be built.

Bearings, gearboxes and actuators
are the invisible muscles of modern
robotics.

An Issue of Industrial Sovereignty

For Europe, the issue extends far beyond financial considerations.

Control over critical components will determine part of the competitiveness of its robotics industry.

Excessive dependence on a small number of foreign suppliers could weaken the entire value chain, as demonstrated by recent semiconductor shortages and supply-chain tensions.

Conversely, having European companies capable of producing bearings, gearboxes, sensors and transmission systems represents a genuine strategic advantage.

Robotics sovereignty is not simply about manufacturing robots. It also means controlling the invisible technologies that make them possible.

An Opportunity for European Industry

European groups such as SKF, Schaeffler, THK Europe, Bosch Rexroth and SEW-Eurodrive already possess recognised expertise in high-precision mechanical components.

As the markets for industrial robotics, humanoid robots, autonomous logistics and medical robots continue to accelerate, these manufacturers could benefit from structurally growing demand.

From this perspective, the future leaders of robotics may not be limited to the robot manufacturers showcased at international trade fairs. The companies designing and producing essential components could become the true pillars of this new industrial revolution.

Robotics is often presented through its most spectacular machines. Yet its success will depend above all on a supply chain capable of producing millions of reliable, precise and competitively priced components.

In the future, value may not be concentrated solely in the robots themselves, but also in the technologies that power and animate them. For European manufacturers, the real challenge is therefore to preserve and strengthen this expertise, ensuring that Europe remains a major player in the next robotics revolution rather than becoming merely an assembler of technologies designed elsewhere.

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For decades, the technological singularity belonged more to the realm of science fiction than engineering. Today, the concept has become a subject of discussion in artificial intelligence laboratories, major industrial corporations, investment funds, and robotics companies around the world.

The spectacular acceleration of generative artificial intelligence, autonomous robots, foundation models, and AI agents has revived a fundamental question: what will happen when machines become capable of improving their own intelligence faster than humans can?

No one can say with certainty whether this “singularity” will ever occur. One thing, however, is certain: current advances in AI are forcing industrial companies to consider this possibility no longer as a philosophical curiosity, but as a genuine strategic issue.

A Sixty-Year-Old Theory

The origins of the technological singularity date back to 1965, when British mathematician I. J. Good, a former colleague of Alan Turing, imagined that an “ultra-intelligent machine” capable of designing even more powerful machines would trigger a genuine intelligence explosion.

In the 1990s, mathematician and author Vernor Vinge further developed this idea, arguing that once artificial intelligence reached a certain level, technological development would simply become impossible for humans to predict.

The concept was later popularised by Ray Kurzweil, now a principal researcher at Google, who believes that artificial general intelligence, or AGI, could emerge within the next few decades before gradually leading to the continuous self-improvement of intelligent systems.

Unlike today’s AI systems, which remain specialised despite their impressive performance, an AGI would be capable of learning, reasoning, and solving problems across virtually every intellectual field, with versatility comparable or even superior to that of a human being.

The singularity therefore does not simply refer to the moment when AI surpasses humans. It primarily describes the point at which intelligent systems become capable of improving their own algorithms, hardware architectures, research methods, and capacity for innovation.

The true AI revolution may not lie in its ability
to answer our questions, but in its ability to learn
without being shown every step.

Why Has the Debate Become So Serious?

Just five years ago, most industrial robots operated primarily through deterministic programming.

Today, the situation is changing extremely rapidly.

Large language models, vision-language-action models, autonomous agents, and foundation models are gradually enabling robots to understand natural-language instructions, interpret their surroundings, plan complex actions, and learn new tasks without task-specific programming.

Companies such as NVIDIA, Google DeepMind, Tesla, Figure AI, Boston Dynamics, Agility Robotics, Sanctuary AI, and Physical Intelligence are now investing billions of dollars in developing this new generation of intelligent robots.

At the same time, the global robotics market continues to grow.

According to the International Federation of Robotics, more than 4.2 million industrial robots are currently operating in factories around the world, while more than 540,000 new robots are installed every year. Robot density has now reached record levels, particularly in South Korea, Singapore, China, Japan, and Germany.

Goldman Sachs and Morgan Stanley estimate that the humanoid robot market could be worth tens of billions of dollars annually by 2035, with potentially several million robots deployed in manufacturing, logistics, healthcare, and service industries.

These figures explain why the singularity is no longer merely an academic subject.

The Computing-Power Revolution

One of the main drivers of this acceleration is the spectacular increase in available computing power.

The most advanced AI models are now trained on infrastructures composed of tens of thousands of graphics processing units, or GPUs.

Supercomputers dedicated to artificial intelligence now achieve performance measured in exaFLOPS while consuming several hundred megawatts of electricity.

The cost of training the most advanced models, which amounted to only a few million dollars several years ago, has now reached several hundred million dollars. Some analysts even estimate that future generations of models will require investments exceeding one billion dollars.

At the same time, every new generation of AI chips developed by companies such as NVIDIA and AMD significantly improves performance while reducing energy consumption per operation.

This simultaneous progress in hardware, algorithms, and infrastructure creates a virtuous cycle that continuously accelerates the capabilities of artificial intelligence.

When Robots Begin to Learn

For a long time, every new robotic task required weeks of programming.

That era is beginning to disappear.

Foundation models designed for robotics now make it possible to transfer knowledge from one task to another.

Instead of programming every movement individually, engineers can provide a few demonstrations or simply describe a task in natural language.

Platforms such as NVIDIA Isaac and the RT models developed by Google DeepMind perfectly illustrate this evolution.

Eventually, each robot could learn from the experiences of thousands of other robots connected to the same artificial intelligence model.

An improvement made in one factory could therefore benefit every other machine deployed around the world almost instantly.

Although this does not yet represent the singularity, this form of collective intelligence already constitutes an early form of large-scale continuous improvement.

The question is no longer simply how to build
more intelligent machines, but how to ensure
that they remain aligned with human interests.

Peter Thiel’s Perspective

Among Silicon Valley investors, PayPal co-founder Peter Thiel has adopted a particularly interesting position.

From an early stage, he financially supported organisations working on advanced artificial intelligence and invested in several major companies in the sector.

Unlike the most optimistic visionaries, Peter Thiel considers the singularity to represent both the greatest economic opportunity in history and the greatest technological risk humanity may ever face.

He has sometimes compared the arrival of artificial superintelligence to a first encounter with an extraterrestrial civilisation: the real issue would not be its power, but its ability to remain compatible with human interests.

Sceptics Point to the Limitations

Not all researchers share this optimism.

Current systems continue to produce errors, struggle to reason over very long periods, consume enormous amounts of computing resources, and still possess only an imperfect understanding of the physical world.

Many specialists believe that simply increasing the size of models will probably not be enough, on its own, to produce genuine general intelligence.

Others point out that physical limitations remain very real, including global semiconductor production, energy consumption, data-centre cooling, the availability of critical metals, and infrastructure costs.

The development of AI could therefore be slowed as much by economic constraints as by scientific challenges.

Alignment: The Real Challenge

Ultimately, the greatest challenge may not be building a more powerful intelligence.

It may be ensuring that it remains aligned with human objectives.

This is the central purpose of AI alignment research, which seeks to make artificial intelligence systems transparent, interpretable, controllable, and safe.

In robotics, this issue is even more critical.

Unlike conversational models, robots interact directly with the physical world.

A logistics robot, surgical robot, or autonomous construction robot will have to make decisions continuously in unpredictable environments, sometimes while working in direct contact with humans.

Safety could therefore become a competitive advantage just as important as the performance of the artificial intelligence itself.

A Transition Rather Than a Sudden Breakthrough

Will the technological singularity occur in twenty years? In fifty years? Or never?

No one can answer that question today.

One reality, however, is already clear: artificial intelligence is transforming robotics at an unprecedented speed.

Robots are becoming adaptive rather than programmed.

Factories are becoming software-driven.

Humanoid robots are gradually leaving laboratories and entering their first industrial sites.

Engineers now work every day with AI assistants capable of generating code, designing mechanical components, optimising trajectories, and analysing thousands of simulation scenarios.

Perhaps history will ultimately remember the singularity not as a sudden event, but as a long transition during which machines gradually stopped being simple tools and became genuine engineering partners.

From this perspective, the real question is no longer whether artificial intelligence will one day surpass human capabilities, but how industrial companies will learn to collaborate with systems capable of innovating alongside them.

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With Robostral Navigate, Mistral is introducing an approach that could transform how mobile robots are designed and deployed. The objective is ambitious: to enable a robot to understand an instruction expressed in natural language, interpret its surroundings using a simple RGB camera, and plan a route to its destination without relying on a complex sensor infrastructure.

A Vision-Centred Approach

Autonomous navigation traditionally relies on several combined technologies: LiDAR, depth cameras, inertial measurement units (IMUs), GPS where available, and simultaneous localisation and mapping algorithms, commonly known as SLAM.

This architecture delivers excellent performance, but it has two major disadvantages: cost and integration complexity.

Robostral Navigate adopts a different philosophy. The model uses only images from a standard colour camera together with natural-language instructions. The AI continuously analyses the video feed, identifies the structural elements of the scene, assesses possible routes, and generates the commands required for navigation.

This Vision-Language-Action, or VLA, approach brings robotics closer to the advances made in recent years by multimodal models, which can reason simultaneously across visual and textual data.

For robot manufacturers, this simplification represents a considerable economic advantage. An RGB camera costs only a few dozen euros, compared with several hundred or even several thousand euros for an industrial LiDAR sensor.

A Compact 8-Billion-Parameter Model

Robostral Navigate is based on an 8-billion-parameter model, offering a balance between computing power and operational efficiency.

Unlike very large general-purpose models that may contain several hundred billion parameters, this smaller size enables faster execution while remaining compatible with embedded platforms that have limited resources.

In robotics, this optimisation is essential. Navigation decisions must be made within a few dozen milliseconds to ensure smooth and safe movement.

The model must therefore:

• interpret camera images in real time
• understand the user’s instruction
• locate the target within its environment
• plan an optimal route
• avoid dynamic and static obstacles
• continuously correct its trajectory

All these processes are handled within an integrated system, without requiring several independent software modules.

With Robostral Navigate, Mistral AI demonstrates that autonomous navigation can rely more heavily on artificial intelligence than on the multiplication of sensors.

Large-Scale Training in Simulation

One of Robostral Navigate’s most remarkable features is its training process.

According to Mistral AI, the model was trained on nearly 400,000 trajectories across more than 6,000 simulated environments.

Simulation-based training has become standard practice in modern robotics. It makes it possible to rapidly generate millions of scenarios that would be impossible to reproduce physically at a reasonable cost.

Virtual robots can therefore learn to navigate buildings, offices, warehouses, factories, and homes with an extremely wide variety of layouts.

Simulated environments can also introduce numerous disruptions, including:

• lighting variations
• unexpected obstacles
• displaced objects
• open or closed doors
• changes in furniture placement
• people moving through the environment

This diversity improves the model’s ability to generalise when faced with unfamiliar situations.

Benchmark-Level Performance

Mistral AI evaluates Robostral Navigate using the Room-to-Room Continuous Environment benchmark, known as R2R-CE, an international reference used to measure the performance of robotic navigation algorithms.

The model achieves:

• a 79.4% success rate in environments already encountered during training;
• a 76.6% success rate in completely new environments.

These results outperform several competing approaches that rely solely on an RGB camera and rival certain architectures that incorporate depth sensors.

This ability to generalise is probably one of the model’s most interesting features. In practice, a robot will need to operate inside buildings it has never encountered before.

Understanding Natural Language

One of the major innovations is the direct use of natural language.

Instead of programming GPS coordinates or selecting a destination on a map, an operator can provide instructions such as:

“Go to the meeting room.”

“Proceed to the shipping area.”

“Go through the corridor, then turn left after the red door.”

The model interprets these instructions, associates them with visual information, and gradually builds a navigation plan.

This development brings robotics closer to everyday use and reduces the technical expertise required to operate autonomous robots.

Reducing dependence on LiDAR could make
mobile robots more accessible and accelerate
their large-scale deployment.

Immediate Industrial Applications

The potential applications are numerous.

In logistics, Robostral Navigate could make it easier for robots to move autonomously between warehouse shelves without requiring extremely precise mapping.

In manufacturing, mobile robots could transport components between different production stations while adapting to changes in their surroundings.

Inspection drones also represent a promising use case. By understanding an instruction such as “inspect the third conveyor line,” they could carry out their mission with greater autonomy.

Quadruped robots designed for industrial inspections or interventions at sensitive sites could also benefit from this technology.

Finally, service robots operating in hotels, hospitals, or office buildings could provide more natural navigation while requiring less specialised infrastructure.

Mistral’s Physical AI Strategy

This announcement confirms Mistral AI’s ambition to become a major player in Physical AI.

After developing language models capable of competing with those created by major American technology companies, the French company is now extending its expertise into autonomous systems.

This strategy comes at a time when the convergence between artificial intelligence and robotics is accelerating. Major technology companies are investing heavily in models capable of connecting perception, reasoning, and action.

The objective is no longer simply to create conversational assistants, but to provide robots with a sufficiently rich understanding of the world to act autonomously within complex environments.

What Challenges Remain?

Despite the promising results, several technical challenges remain.

The first concerns the transition from simulation to the real world, commonly known as Sim-to-Real. Performance achieved in virtual environments must be confirmed on robots operating under real industrial conditions, where lighting, reflections, dust, and unexpected obstacles can interfere with perception.

Robustness in exceptional situations represents a second major challenge. Robots will need to respond safely when they encounter a significantly altered environment or an ambiguous situation.

Finally, the energy consumption and computing resources required to run the model on embedded hardware will be decisive factors in its large-scale adoption.

Robot Magazine’s Analysis

With Robostral Navigate, Mistral AI is not simply seeking to improve autonomous navigation. The company is proposing a new way of designing mobile robots.

By reducing dependence on specialised sensors and placing vision and natural language at the centre of autonomy, the French company is opening the way for platforms that could be simpler, less expensive, and easier to deploy.

Should the performance observed in simulation be confirmed in real-world conditions, this approach could accelerate the democratisation of mobile robots across logistics, industry, critical infrastructure, and service sectors.

More broadly, it reflects a fundamental trend: the future of robotics will increasingly depend on general-purpose AI models capable of perceiving, reasoning, and acting within the physical world.

Robostral Navigate therefore represents an important milestone in the convergence between artificial intelligence and autonomous robotics.

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For years, industrial robots remained confined to production cells behind safety barriers. Today, another category of machines is attracting the attention of manufacturers: AMRs (Autonomous Mobile Robots).

Capable of transporting components, pallets or bins without requiring dedicated infrastructure, they are becoming one of the pillars of Industry 4.0. Their adoption is accelerating in logistics, e-commerce, the automotive industry and even hospitals, driven by labour shortages, the pursuit of productivity gains and advances in artificial intelligence.

Analysts estimate that the global AMR market will exceed $5 billion as early as 2026, with annual growth ranging from 14% to 20%, depending on the research firm. This makes it one of the fastest-growing segments of industrial robotics.

From AGVs to Truly Autonomous Robots

For many years, factories relied on AGVs, or Automated Guided Vehicles vehicles that appeared autonomous, but only to a limited extent.

In reality, these machines follow predefined routes using magnetic strips, QR codes or reflectors installed on the floor. As soon as an obstacle appears, they stop.

AMRs follow an entirely different approach.

Equipped with LiDAR sensors, cameras, depth sensors and real-time mapping algorithms such as SLAM, an AMR builds its own representation of its environment, calculates its route and instantly adjusts it whenever a person, forklift or pallet blocks its path.

This autonomy significantly reduces installation costs and allows industrial sites to be reconfigured quickly without modifying their existing infrastructure.

AMRs no longer simply follow a route.
They understand their environment and
make their own navigation decisions.

Why Are Manufacturers Accelerating Adoption?

The rapid growth of e-commerce and increasing pressure on delivery times have fundamentally changed warehouse requirements.

Companies are now looking for systems capable of absorbing peaks in activity without having to recruit large numbers of additional workers.

AMRs address this challenge directly.

They handle repetitive movements between storage areas, production lines and order-picking stations, allowing operators to focus on higher-value tasks.

This trend is being reinforced by recruitment difficulties in logistics and material-handling roles across most industrialised economies.

Case Study: Decathlon Doubles Its Order-Picking Capacity

One of the most striking examples is Decathlon, which has deployed Skypod robots, developed by Exotec, across several European logistics centres.

In Portugal, order-picking capacity increased from 57,000 to 114,000 orders, while the number of stores supplied rose from 41 to 73.

In the United Kingdom, order pickers now walk less than one kilometre per day, compared with more than 10 kilometres previously, reducing fatigue and improving working conditions.

Another revealing indicator is that the order-picking error rate was cut in half.

Amazon Prepares the Next Generation

At Amazon, autonomous mobile robots are already being used in several dozen logistics centres.

In 2026, the group introduced a new version of its Proteus robot, capable not only of navigating autonomously but also of interpreting instructions expressed in natural language through artificial intelligence.

The objective is no longer simply to move loads, but to enable robots to plan their own missions and routes according to operational priorities.

Benefits Observed in Real-World Operations

Initial feedback shows several recurring benefits:

• Reduced unnecessary movement for operators
• Improved safety through obstacle detection
• Faster deployment than a traditional AGV system
• Greater flexibility when production requirements change
• Continuous operation, including during night shifts and off-peak hours

Some market studies indicate that more than 60% of companies investing in robotics achieve productivity gains exceeding 25%, while investment in warehouse automation continues to rise sharply.

AMRs do not replace operators: they optimise
their movements and improve their working
conditions.

Significant Challenges Remain

However, AMRs are not a universal solution.

Integration with information systems such as ERP, MES and WMS platforms, cybersecurity, multi-robot fleet management and safe interaction with human operators remain major challenges.

Research is now focusing on robots capable of understanding the behaviour of people around them so they can adjust their trajectories in an even smoother and safer way.

Towards Fully Autonomous Logistics?

The future of AMRs is no longer limited to transporting pallets.

Future generations are expected to cooperate with robotic arms, inventory drones and, eventually, humanoid robots capable of carrying out complete order-picking operations.

In this vision, the AMR becomes the mobile platform of the smart factory, connecting every workstation through a continuous flow controlled by artificial intelligence.

Far from being a passing trend, autonomous mobile robots are now establishing themselves as one of the most cost-effective and rapidly deployable technologies in industrial robotics. For many companies, the question is no longer whether they will adopt AMRs, but how quickly they will be able to integrate them into their operations.

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On July 7, Paris will host the inaugural edition of MACHINA, a new summit entirely dedicated to Physical AI. Held at Station F, one day before the RAISE Summit, the event reflects a growing conviction shared across the industry: the next technological revolution will be driven by intelligent machines, not just intelligent software.

The organizers have assembled one of the strongest speaker lineups ever seen in Europe for a robotics-focused event.

Among the first confirmed speakers are Jim Fan, Director at NVIDIA and one of the world’s leading voices on Physical AI; Marc Raibert, founder of Boston Dynamics and now Executive Director of the RAI Institute; Carolina Parada, Head of Robotics at Google DeepMind; Jeff Cardenas, Co-founder and CEO of Apptronik; Bernt Børnich, Founder and CEO of 1X; Jonathan Hurst, Co-founder of Agility Robotics; David Reger, Founder and CEO of NEURA Robotics; Abhinav Gupta, President and Co-founder of Skild AI; Thomas Wolf, Co-founder and Chief Science Officer of Hugging Face; Laura Modiano, Head of Founder Experience at OpenAI; and Dr. Péter Fankhauser, CEO of ANYbotics.

After generative AI transformed the
digital world, Physical AI is set to transform
the physical one.

Bringing together pioneers from NVIDIA, Google DeepMind, Boston Dynamics, Hugging Face, OpenAI, Apptronik, Agility Robotics, 1X, NEURA Robotics and ANYbotics under one roof sends a clear message: Physical AI is no longer an emerging niche. It is becoming one of the fastest-moving sectors in artificial intelligence.

The timing could hardly be better. Around the world, investment in robotics is accelerating as breakthroughs in foundation models, computer vision, simulation and digital twins allow robots to perform increasingly complex tasks. The race is no longer about building better machines alone, but about giving those machines the intelligence to understand and interact with their environment.

For Europe, MACHINA also carries strategic significance. While the United States has companies such as Apptronik, Agility Robotics and Boston Dynamics, and Asia continues to dominate industrial robotics manufacturing, Europe is seeking to establish itself as a leader in embodied intelligence. The presence of companies such as NEURA Robotics, ANYbotics and major European industrial groups illustrates that ambition.

Unlike traditional robotics exhibitions, MACHINA is designed around ideas rather than products. The discussions are expected to focus on the technologies that will define the next decade of robotics: foundation models for robots, simulation, digital twins, AI infrastructure, autonomous systems, safety, industrial deployment and human-robot collaboration.

The global race is no longer about building
better robots alone it is about giving those
robots the intelligence to understand and interact
with the real world.

The event also reflects a broader shift in the AI industry. For the past three years, the conversation has revolved around chatbots and generative models. Increasingly, however, the industry’s biggest players including NVIDIA, Google DeepMind and OpenAI are investing in AI that can operate beyond the digital world.

The objective is no longer simply to answer questions, but to enable machines to understand, move and work alongside humans.

Whether MACHINA becomes Europe’s equivalent of NVIDIA GTC for robotics remains to be seen.

But its inaugural edition has already succeeded in attracting many of the people shaping the future of intelligent machines.

Robot Magazine will follow MACHINA closely, bringing readers the latest developments from one of the most anticipated Physical AI events of the year.

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For several years, industrial robotics has been experiencing unprecedented acceleration. Artificial intelligence is no longer limited to analyzing data or generating text: it now controls machines capable of perceiving their environment, making real-time decisions, and interacting directly with human beings.

This evolution, often described as “physical AI,” is paving the way for a new generation of industrial robots, autonomous mobile robots, and humanoid robots.

But this transformation also comes with a major challenge. The more autonomous robots become, the greater the safety requirements. Unlike traditional robots enclosed behind protective barriers, these new machines will operate directly alongside workers, share the same workspaces, and collaborate with them on daily tasks.

In this context, safety is becoming a strategic issue just as important as computing power or algorithmic performance.

It is precisely to address this challenge that NVIDIA unveiled Halos for Robotics, presented as the first complete safety system designed for robots powered by physical artificial intelligence.

As robots become increasingly autonomous,
safety is becoming the true technological
frontier of physical artificial intelligence.

The announcement, made during Automate 2026 in Chicago, confirms the American tech giant’s ambition to become not only the global leader in AI platforms, but also one of the key players in robotic safety. This initiative could profoundly change the way manufacturers design future generations of robots and accelerate their deployment across industry.

When robots leave their protective cages

For several decades, industrial robot safety was based on a simple principle: keeping humans away from machines. Robots were installed in closed cells, surrounded by fences, optical barriers, and emergency stop systems. This approach was suited to robotics mainly dedicated to repetitive tasks such as welding, painting, or heavy material handling.

Today, the situation is changing rapidly. Manufacturers are looking for robots capable of working directly with operators in order to improve production line flexibility. Mobile robots transport components between different workshops, cobots assist technicians in their daily tasks, and humanoid robots are beginning to perform order preparation, handling, and quality control operations.

However, this proximity creates new risks. Robots must be able to detect an operator crossing their path, anticipate their movements, recognize unexpected objects, and adapt their behavior within milliseconds. Traditional safety methods are therefore reaching their limits. Manufacturers no longer simply want robots to stop when danger appears; they want them to be capable of avoiding dangerous situations before they even occur.

According to several analyst firms, the global industrial robotics market is expected to exceed $80 billion by the end of the decade, driven in particular by the rise of smart factories, automated warehouses, and humanoid robotics. However, this growth can only continue if trust in these systems progresses at the same pace as their performance.

The rise of humanoid robots is profoundly
transforming the certification requirements
for robotic systems.

Halos: a safety platform designed from the ground up

With Halos for Robotics, NVIDIA is proposing a radically different approach. Rather than treating safety as an additional layer added at the end of development, the company integrates it directly into the entire robotic architecture.

The platform covers the full technological chain. It takes into account computing hardware, specialized processors, sensors, safety software, artificial intelligence models, simulation tools, and the validation processes required for the certification of robotic systems.

This “full-stack” vision is one of the project’s main innovations. The different components communicate with each other to provide continuous monitoring of the robot’s operation and its environment. If an anomaly is detected, the system can immediately take the appropriate measures while maintaining a high level of operational availability.

NVIDIA also explains that Halos builds on development methods used for several years in the field of autonomous vehicles. Validation procedures, risk analyses, and software tools developed for self-driving cars are now being adapted to the specific needs of industrial robotics. The company says this expertise represents more than 18,600 cumulative years of engineering work dedicated to functional safety.

This reuse of already proven technologies makes it possible to accelerate the market launch of robots that meet future regulatory requirements, while limiting development costs for manufacturers.

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Long confined to research laboratories or spectacular promotional videos, humanoid robots now seem ready to take a decisive step: their integration into professional environments.

AI leaves Screens to enter the Physical World

Since the arrival of ChatGPT in 2022, artificial intelligence has mainly been associated with software, virtual assistants and content generation. But at VivaTech 2026, attention shifted toward a new category of technologies: “Embodied AI,” or embodied artificial intelligence.

The idea is simple: to combine the reasoning capabilities of AI models with robotic bodies capable of acting in the real world.

This convergence between artificial intelligence and robotics is probably one of the most important technological developments of the decade. Robots are no longer programmed solely to repeat predefined movements. They are now learning to understand their environment, adapt to unexpected situations and collaborate with humans.

For many observers present in Paris, this evolution recalls the beginnings of personal computing in the 1980s or the arrival of the smartphone in the late 2000s.

KANGAROO, a European Showcase for Advanced Robotics

Among the most notable demonstrations was KANGAROO, the robot developed by PAL Robotics in partnership with several European research centers.

Equipped with advanced mobility and reinforcement learning-based capabilities, KANGAROO represents Europe’s determination to remain competitive in the global humanoid robotics race.

Unlike some highly publicized demonstrations from Asia or the United States, the European approach appears more pragmatic. The goal is not only to build a spectacular robot, but to develop a platform capable of being used in industrial, logistics or scientific environments.

This presence also served as a reminder that Europe still has recognized expertise in industrial robotics, automation and artificial intelligence research.

At VivaTech 2026, one thing became clear:
AI no longer wants only to understand the world.
It now wants to act in the physical world.

China accelerates and sets the Pace

If there is one major lesson to take from this edition, it is undoubtedly the rise of Chinese players.

While China already largely dominated global industrial robot production, it is now investing massively in humanoid robots.

Several Chinese companies used VivaTech to demonstrate their progress in Embodied AI. Their objective is ambitious: to produce versatile robots capable of carrying out a wide variety of tasks in factories, warehouses and logistics centers.

This strategy is part of a long-term national industrial policy. Beijing now considers robotics to be a strategic sector, on the same level as semiconductors or artificial intelligence.

For European industrial players present at the event, the conclusion is clear: competition is no longer based solely on robot quality, but also on the ability to produce at scale and rapidly reduce costs.

Robots leave the laboratories

One of the major differences between VivaTech 2026 and previous editions lies in the maturity of the demonstrations presented.

Just a few years ago, visitors mainly discovered experimental prototypes or futuristic concepts.

This year, the use cases were much more concrete.

The robots presented were designed to:

• Assist operators in warehouses;
• Handle heavy loads;
• Perform repetitive tasks;
• Take part in industrial maintenance;
• Improve logistics operations;
• Support certain hospital activities.

This evolution reflects a paradigm shift. Companies are no longer simply trying to impress investors. They are now seeking to demonstrate the economic value of their solutions.

Return on investment is becoming just as important an argument as technological performance.

The challenge of labor shortages

Another topic widely discussed in the aisles of the event was the global shortage of skilled workers.

Many industrial sectors are struggling to recruit enough operators, technicians or material handlers. This situation is particularly visible in Europe, where demographic ageing is increasing pressure on the labor market.

For several manufacturers, humanoid robots could provide a partial response to this challenge.

The objective is not necessarily to replace human workers, but to automate certain difficult, repetitive or dangerous tasks.

This approach explains why the first markets targeted by humanoid robot manufacturers are mainly logistics, industry and critical infrastructure.

France wants to take its place

Faced with massive investments from the United States and China, France is also trying to strengthen its position.

Companies such as Mirokaï, Wandercraft and several research laboratories supported by the CNRS used VivaTech as a showcase for their expertise.

The message is clear: France wants to actively participate in building the next generation of intelligent robots.

This ambition is part of a broader dynamic of reindustrialization and European technological sovereignty.

Because behind the spectacular demonstrations lies a major economic challenge. According to several analysis firms, the global humanoid robotics market could represent several hundred billion dollars over the coming decades.

The United States dominates AI. China is accelerating industrialization. Europe is relying on its scientific
and industrial expertise.

A Revolution comparable to the arrival of the PC?

For some experts present at VivaTech, humanoid robotics could follow a trajectory similar to that of personal computers.

The first models remain expensive and relatively limited. But rapid advances in artificial intelligence, batteries, sensors and mechanical systems suggest that costs will gradually decline.

As prices fall, use cases should multiply.

Companies experimenting with these technologies today could therefore gain a significant competitive advantage in the years ahead.

Toward a new Robotic economy

VivaTech 2026 will probably be remembered as a turning point for robotics.

The event showed that humanoid robots are no longer mere technological curiosities. They are gradually becoming industrial tools capable of addressing very concrete challenges: labor shortages, productivity, safety and competitiveness.

The global race is now underway.

The United States has powerful players backed by AI giants. China is accelerating thanks to its industrial capacity. Europe is trying to preserve its position through its scientific and industrial expertise.

One thing is certain: the robots seen in the aisles of VivaTech probably represent only a glimpse of what awaits companies over the next decade.

The question is no longer whether humanoid robots will become part of our professional daily lives.

The real question is now which countries and which companies will succeed in taking the lead in this new industrial revolution.

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As humanoid robots continue to dominate headlines and attract billions in investment worldwide, one critical question remains unanswered: when will these machines move beyond impressive demonstrations and become a common reality in factories, warehouses, hospitals, and construction sites?

This is precisely the challenge that the Advanced Humanoid Forum 2027 aims to address. Scheduled to take place on January 20–21, 2027, in Munich, Germany, the event was founded by industry expert Ulrich Moeller with a clear mission: bringing together the entire humanoid robotics ecosystem to accelerate the transition from simulation to real-world industrial deployment.

The Next Challenge: Leaving the Laboratory

Recent advances in artificial intelligence, computer vision, sensing technologies, and actuator systems have enabled the emergence of a new generation of humanoid robots.

Companies such as Tesla, Figure AI, Agility Robotics, Apptronik, Sanctuary AI, and Unitree have demonstrated increasingly capable machines that can walk, manipulate objects, and interact with complex environments.

However, industrial adoption remains limited.

The challenge is no longer teaching a robot how to walk or pick up an object. The real challenge lies in ensuring reliability, safety, productivity, and return on investment under real operating conditions.

The Advanced Humanoid Forum focuses on exactly this critical phase: validating humanoid robots in practical industrial applications.

Munich: A Future European Hub for Humanoid Robotics

Germany is uniquely positioned to host such an event.

As Europe’s largest industrial economy, the country combines world-leading automotive manufacturers, industrial automation specialists, robotics companies, research institutions, and engineering expertise.

The forum aims to create a meeting point for:

• Humanoid robot manufacturers
• AI developers
• Industrial end-users
• Research organizations
• Investors
• System integrators and technology providers

Rather than focusing on futuristic concepts, the event emphasizes practical deployment strategies and measurable business outcomes.

AI Is Driving the Humanoid Revolution

Traditional industrial robots excel in repetitive and highly structured environments. Humanoid robots, however, are designed to operate in spaces originally built for humans.

To achieve this, they rely on multiple technological layers:

Large AI models allow them to understand complex instructions.

Advanced computer vision systems enable real-time perception of their surroundings.

Digital twins and simulation environments help robots learn and optimize behaviors before deployment.

Cloud and edge computing infrastructures support fleet management and continuous learning.

One of the key topics of the forum will be how these technologies can be integrated into robust systems capable of delivering real business value.

The First Markets Ready for Humanoids

Despite public fascination with humanoid assistants in homes, experts generally agree that the first large-scale deployments will occur in industrial and commercial environments.

Manufacturing

Factories worldwide are facing growing labor shortages and increasing pressure to improve productivity. Humanoid robots could assist with material handling, assembly operations, inspection tasks, and repetitive workflows.

Logistics

Warehouses represent one of the most promising deployment environments. Humanoids could complement autonomous mobile robots by performing tasks that require human-like mobility and dexterity.

Healthcare

Aging populations and staffing shortages are creating demand for robotic assistants capable of supporting healthcare professionals and helping patients with daily activities.

Construction

Construction sites remain highly complex and unpredictable environments. Future humanoids could assist workers by carrying materials, operating tools, and performing repetitive physical tasks.

Europe’s Opportunity in the Global Humanoid Race

One of the central discussions at the forum will focus on Europe’s position in the global humanoid robotics market.

The United States currently benefits from highly visible players such as Tesla, Figure AI, and Apptronik. Meanwhile, China is investing heavily in humanoid robotics through large-scale industrial policies and government support.

Europe, however, possesses several important advantages:

• Deep industrial expertise
• World-class research institutions
• Global leaders in automation and engineering
• A strong ecosystem of innovative technology companies

The challenge now is transforming these assets into globally competitive humanoid robotics champions.

A Defining Moment for the Industry

The timing of the Advanced Humanoid Forum could not be more relevant.

After years of spectacular demonstrations, the humanoid robotics sector is entering a phase where industrial users demand concrete answers about productivity, safety, reliability, and business value.

Under the leadership of Ulrich Moeller, the forum seeks to become a key platform for companies looking to understand, develop, and deploy the next generation of intelligent robots.

For manufacturers, investors, engineers, and technology leaders, one thing is becoming increasingly clear: the question is no longer whether humanoid robots will enter our workplaces, but how quickly they will transform them.

As Europe seeks to define its role in this emerging market, initiatives such as the Advanced Humanoid Forum may play a crucial role in shaping the future of humanoid robotics on the continent.

We invite you to attend : https://advancedhumanoidforum.com/

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Behind this concept lies a simple but powerful idea: creating an extremely accurate virtual replica of a physical asset in order to understand, test, predict and optimize its behavior before taking action in the real world. What once belonged to science fiction is now becoming an operational reality in factories, energy infrastructures, logistics centers and the most advanced robotics programs.

The global digital twin market is also experiencing spectacular growth. According to several specialized research firms, it could exceed 100 billion dollars by the end of the decade. Manufacturers see it as a way to reduce costs, improve productivity and accelerate innovation in a context where international competition is intensifying.

From 3D Model to Living Digital Mirror

For a long time, manufacturers have used 3D models to design their products or facilities. The digital twin goes much further. It is no longer simply a graphical representation, but a living system continuously fed by real data.

Every sensor installed on a machine, every operating robot and every piece of information collected from the field can enrich the digital model. The model then evolves in real time and faithfully reproduces the behavior of its physical counterpart.

Engineers therefore gain unprecedented visibility over their operations. They can observe the condition of a machine, monitor its performance, detect anomalies and anticipate potential failures before they occur.

This predictive capability is now one of the main drivers behind the adoption of the technology. In many industries, just a few hours of production downtime can cost several hundred thousand euros. Being able to anticipate a breakdown before it happens therefore represents a major economic advantage.

The Virtual Laboratory of Modern Industry

Beyond maintenance, the digital twin offers manufacturers a unique testing environment.

Before modifying a production line, integrating a new robot or increasing manufacturing rates, engineers can virtually test dozens of scenarios. Each hypothesis is simulated in the digital environment before being applied on the factory floor.

This approach significantly reduces the risks associated with industrial projects. Design errors are detected earlier, investments are optimized and commissioning times are shortened.

In some sectors such as aerospace and automotive, digital twins are now used from the earliest design phases. Companies can build and operate an entire factory virtually before the first shovel even hits the construction site.

The result is spectacular: fewer unexpected problems, better cost control and a faster innovation cycle.

Robots attract attention.
Digital twins are truly transforming
industry.

When the Digital Twin Becomes the Training Ground for Robots

One of the most fascinating applications of the digital twin today concerns advanced robotics.

Training a robot in the real world is a complex and costly process. Every error can cause material damage, slow down production or require human intervention. Manufacturers are therefore looking to move part of this learning process into virtual environments.

This is precisely the strategy adopted by Dassault Systèmes. With its 3DEXPERIENCE platform, the French group is developing digital twins capable of reproducing the complete behavior of a robot, its working environment and its interactions with human operators.

In this approach, the robot is first tested in its digital double. Engineers can analyze its movements, identify its limitations and optimize its performance before physical deployment.

This logic becomes even more impressive with the arrival of humanoid robots. In 2026, the German manufacturer NEURA Robotics announced a strategic partnership with Dassault Systèmes to integrate its 4NE1 humanoid into an advanced digital twin environment.

The objective is to allow the robot to learn virtually. Every gesture, every movement and every interaction with its environment can be simulated thousands of times. Errors are corrected in the digital world before the robot ever faces reality.

This method considerably reduces development costs while accelerating machine learning. It could become a standard for the next generation of industrial and humanoid robots.

Schneider Electric Is Preparing the Smart Factories of Tomorrow

The digital twin is not only about robots. It is also transforming the way manufacturers design and manage their infrastructures.

Among the most advanced players is Schneider Electric. The French group is investing heavily in technologies that make it possible to virtually reproduce complete industrial installations.

The objective is to create a global view of the factory in order to optimize energy performance, production flows and equipment maintenance.

Humanoid robots will first learn
in virtual worlds before
evolving in the real world.

In collaboration with NVIDIA and ETAP, Schneider Electric recently presented a particularly ambitious project: a digital twin capable of modeling the full energy needs of infrastructures dedicated to artificial intelligence.

Thanks to this technology, manufacturers can visualize in real time the interactions between electrical networks, IT systems and production equipment. They are then able to identify weak points, reduce unnecessary energy consumption and improve the overall efficiency of the site.

This approach perfectly illustrates the evolution of the digital twin. It is no longer just a simulation tool, but a true control center capable of continuously optimizing industrial operations.

Europe Has a Major Card to Play

Faced with the rise of American and Chinese players in artificial intelligence, Europe has a strategic advantage that is often underestimated.

The continent is home to several global leaders in industrial software, automation and digital simulation. Dassault Systèmes, Schneider Electric, Siemens and ABB all possess unique expertise in modeling complex systems.

The future of robotics may depend as much on digital platforms as on the machines themselves.

Tomorrow’s robots will need to be trained, tested and optimized in increasingly sophisticated virtual environments. Companies capable of building these digital worlds will have a considerable advantage in global competition.

As robots become more autonomous and artificial intelligence becomes integrated into industrial processes, the digital twin is emerging as the invisible layer that connects the physical world to the virtual world.

The Hidden Brain of Industry 5.0

The industry of the future will not only be made up of robots, sensors and artificial intelligence. It will also rely on a permanent digital representation of its operations.

Every important machine could soon have its own digital twin. Every robot could be trained in a virtual universe before deployment. Every factory could be continuously simulated in order to optimize its performance and reduce its energy footprint.

This revolution is already underway.

While humanoid robots represent the visible part of industrial transformation, digital twins are probably its invisible brain. They allow companies to predict rather than endure, to test rather than risk, and to innovate faster than ever before.

In the coming years, the most efficient factories will not necessarily be those with the largest number of robots. They will probably be those with the best digital twins.

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Europe is accelerating its artificial intelligence ambitions. From June 30 to July 1, 2026, Berlin will host the second edition of GITEX AI Europe, one of the continent’s largest events dedicated to artificial intelligence, cybersecurity, cloud computing and emerging technologies.

The event is expected to bring together nearly 950 enterprises and startups, more than 600 international investors, and over 150 speakers from 80 countries. The gathering highlights Europe’s growing determination to strengthen its technological sovereignty and compete with the AI giants of the United States and Asia.

Why Berlin?

Berlin has become one of Europe’s most dynamic technology hubs. According to figures highlighted by the organizers, the city is home to 57 unicorns, more than 9,000 AI professionals, and a startup ecosystem valued at approximately €169 billion.

This makes Berlin a natural location for an event focused on the future of artificial intelligence, innovation, and digital transformation.

Franziska Giffey, Berlin’s Vice Mayor and Senator for Economic Affairs, believes the event will reinforce the city’s position as a gateway for international investment, technological collaboration, and sustainable economic growth.

OpenAI, Google, AWS and Salesforce Among the Major Participants

The exhibition floor will feature some of the world’s leading technology companies, including:

• OpenAI
• Google
• AWS
• Cloudflare
• Hewlett Packard Enterprise (HPE)
• Salesforce
• Red Hat
• ManageEngine

Visitors will have access to hands-on workshops, AI masterclasses, cybersecurity sessions, and practical demonstrations showcasing how artificial intelligence is transforming industries worldwide.

The event will also welcome national technology pavilions from countries including Austria, Canada, Greece, and Japan, reflecting the increasingly international nature of Europe’s AI ecosystem.

Europe’s AI Champions Take Center Stage

One of the most anticipated European participants is DeepL, widely recognized as one of the world’s most accurate AI-powered translation platforms.

As global businesses continue to operate across multiple languages and markets, companies like DeepL demonstrate that European AI innovation can successfully compete on a global scale.

The company’s presence also highlights a broader trend: Europe is increasingly focused on building its own AI champions rather than relying exclusively on technologies developed elsewhere.

Sovereign AI and Infrastructure in the Spotlight

Beyond product launches and demonstrations, GITEX AI Europe will serve as a strategic forum where policymakers, industry leaders, and investors discuss the future of Europe’s digital economy.

Key topics will include:

• Sovereign AI strategies
• Data infrastructure
• Compute capacity
• Energy requirements for AI
• European hyperscale models
• Cybersecurity
• High-impact industrial AI applications

Germany’s Federal Minister for Digital Transformation and State Modernisation, Dr. Karsten Wildberger, is expected to address the challenges of building the infrastructure required to support Europe’s long-term AI ambitions.

One Trillion Dollars in Investment Capital

A major attraction of the event will be the presence of more than 600 investors managing approximately US$1 trillion in assets.

Through the North Star Europe startup program, over 500 startups specializing in industrial AI, deep tech, quantum security, and advanced digital technologies will have the opportunity to connect with venture capital firms, corporate investors, and strategic partners.

This makes GITEX AI Europe one of the most significant startup and investment platforms in the region.

From Search Engines to “Action Engines”

One of the most closely watched speakers will be Ryan Foutty, VP of Business at Perplexity.

Foutty argues that the next evolution of AI will move beyond traditional search and answer engines toward “action engines” intelligent systems capable of completing tasks on behalf of users through autonomous AI agents.

This vision aligns with the rapid rise of Agentic AI, a new generation of systems designed not only to provide information but also to execute workflows, automate decisions, and perform meaningful work independently.

The Bigger Picture

GITEX AI Europe 2026 demonstrates that Europe is no longer content to be a spectator in the global AI race.

The event reflects a continent investing heavily in innovation, infrastructure, talent, and technological sovereignty. As AI continues to reshape industries ranging from manufacturing and robotics to healthcare and finance, Berlin is positioning itself as one of the key centers driving Europe’s intelligent economy.

For technology leaders, startups, investors, and policymakers, GITEX AI Europe may well provide a glimpse into the future of artificial intelligence in Europe—and beyond.

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