For decades, robotics remained primarily industrial. Robotic arms performed precise tasks in perfectly controlled environments, generally kept at a distance from humans.

That model is changing rapidly.

Advances in artificial intelligence, computer vision, sensors and gripping systems are now making it possible to imagine far more versatile machines. Mobile robots, cobots and humanoids are beginning to move beyond environments in which every movement had to be programmed in advance.

This is a major shift. If robots become capable of performing a wide variety of tasks, they will no longer represent merely an industrial investment. They will gradually become a new form of workforce.

An Industrial Battle as Much as a Technological One

In the United States, investment in artificial intelligence is now extending into robotics. Considerable sums are being committed to humanoids and “Physical AI.”

China, meanwhile, is advancing with a strongly industrial approach. It possesses an advantage that is difficult to ignore: a supply chain capable of producing motors, batteries, sensors, electronics and mechanical components on a massive scale.

The competition will therefore not simply be about who can showcase the most impressive humanoid in a video.

What matters is control over the entire value chain: components, manufacturing, software, AI models, training data, maintenance and the ability to produce thousands and potentially millions of machines.

This is where Europe must position itself.

Europe Already Holds Many of the Cards

It would be wrong to suggest that Europe is absent from the robotics sector.

It has renowned manufacturers, excellent research laboratories and numerous specialised companies. Europe also benefits from one crucial advantage: a substantial industrial base.

Automotive, aerospace, logistics, energy, healthcare and food production are all sectors that could become real-world testing grounds for the next generation of robots.

The real difficulty lies elsewhere.

Europe knows how to develop remarkable technologies and companies. But when it comes to raising billions, industrialising at great speed and conquering a global market, the same obstacles repeatedly emerge: fragmentation of the European market, access to capital, regulatory complexity and insufficient coordination between countries.

This is precisely what makes the comparison with Airbus so relevant.

An “Airbus of Robotics” Really?

The expression may sound excessive. Obviously, the idea is not to bring every European robotics manufacturer together within a single group.

But the history of Airbus is worth considering.

Faced with an extremely capital-intensive industry dominated by American companies, several European countries chose to pool their industrial expertise, funding and ambitions.

Robotics could justify a similar approach.

Not necessarily to create a single European humanoid, but to secure the technologies the entire industry will require: actuators, motors, sensors, vision systems, embedded electronics, AI models designed for the physical world and control software.

The objective should be relatively straightforward: to ensure that Europe retains the ability to build and control its own robotic systems.

Airbus Was Not Created Because Europe Did Not
Know How to Build Aircraft. It Was Created Because
Europe Needed to Achieve Critical Mass. Robotics May Soon Face the Same Question.

Europe’s Future Champions May Already Exist

Talking about an “Airbus of Robotics” might suggest that a new industrial giant needs to be created from scratch.

That is probably unnecessary.

Europe already has several companies developing some of the essential building blocks of this emerging industry.

In Germany, Agile Robots is one of the companies to watch. Based in Munich and originating from research conducted at the German Aerospace Center (DLR), the company develops solutions combining artificial intelligence, robotics and industrial automation. With its Agile ONE humanoid, it has made its ambition clear: to bring this new generation of machines into industrial environments.

Also in Germany, NEURA Robotics is developing cognitive robots, cobots and humanoids with a similar ambition: creating machines capable of working more naturally alongside humans. Its 4NE1 humanoid illustrates its determination to position Germany among the leading players in Physical AI.

Spain also has a long-established company in the field: Barcelona-based PAL Robotics. For more than 20 years, it has developed humanoid, mobile and service robots for research, healthcare, industry and retail.

And many more companies could be added to this list.

Europe also has specialists in industrial automation, vision systems, sensors, semiconductors, software, mechatronics and embedded systems. Long-established industrial groups could also play a major role in this transformation.

Perhaps this is where the true idea behind an “Airbus of Robotics” lies.

Europe does not necessarily need a single company to manufacture a European robot. Instead, it may need an industrial alliance capable of bringing together its strongest players around a common architecture: hardware, critical components, artificial intelligence, training data, software and manufacturing capacity.

Airbus was not created because Europe did not know how to build aircraft. It was created because Europe needed to achieve critical mass to compete with American champions.

Robotics may soon face the same question.

Will Agile Robots, NEURA Robotics, PAL Robotics and Europe’s other champions remain individual companies operating in their respective markets, or could they one day become the building blocks of a genuine European robotics powerhouse?

When Robots Become Economic Infrastructure

Imagine this market 10 or 15 years from now.

If millions of robots are working every day in factories, warehouses, farms, hotels and healthcare facilities, each robot will represent several thousand hours of potential work every year.

At that scale, the issue becomes economic.

A company that depends entirely on a foreign supplier to automate its production also depends on that supplier for part of its ability to produce.

If entire sectors of the European economy eventually rely on machines, software and artificial intelligence models controlled from abroad, the question of sovereignty will inevitably arise.

We have already had this debate concerning semiconductors, cloud computing and, more recently, artificial intelligence.

It would be unfortunate to wait until that dependency has become firmly established before beginning to think seriously about robotics.

Manufacturing Robots May Not Be Enough

One final issue has received relatively little attention so far: who will control the work carried out by these machines?

As robots become increasingly autonomous, platforms will be needed to assign tasks to them, measure their work, supervise their activities and coordinate entire fleets.

In the future, a company may manage three types of resources: employees, artificial intelligence agents and robots.

Humans will remain essential for certain tasks. For others, an AI agent will be more effective. And whenever an action needs to be performed in the physical world, a robot may take over.

Value could therefore gradually shift towards those capable of orchestrating these different forms of work.

This may be where part of the next technological battle will take place.

We Should Not Wait Ten Years to Ask the Question

Robotic sovereignty obviously does not mean that every screw, motor or sensor must be manufactured on European soil.

It means retaining sufficient expertise, technology and industrial capacity to avoid becoming entirely dependent on decisions made elsewhere.

For now, the market remains open. Standards have not yet been definitively established, humanoids are still searching for a viable business model and many potential applications have yet to be invented.

Europe therefore still has a card to play.

But if robotics follows the path many expect, the question will soon no longer be whether robots will occupy an important place in our economy.

We will need to know who builds them, who controls their intelligence, who owns their data and who decides what they are allowed to do.

After cloud computing, semiconductors and artificial intelligence, robotic sovereignty could become one of Europe’s next major industrial challenges.

Cet article Robotic Sovereignty: Does Europe Need Its Own “Airbus of Robotics”? est apparu en premier sur Robot Magazine.

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.

Cet article Mistral AI launches Robostral Navigate est apparu en premier sur Robot Magazine.

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.

Cet article What is an AMR? est apparu en premier sur Robot Magazine.

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.

Cet article MACHINA 2026: Can Paris Become Europe’s Capital of Physical AI ? est apparu en premier sur Robot Magazine.

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.

Cet article NVIDIA Halos for Robotics: Industrial Robot Safety Enters a New Era est apparu en premier sur Robot Magazine.

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/

Cet article Advanced Humanoid Forum 2027 in Germany est apparu en premier sur Robot Magazine.

But behind the numbers, one question stands out: is Figure AI building something fundamentally new, or is it reproducing the hype cycle we have come to know with generative AI?

The honest answer is: probably both at once. And that is precisely what makes this company so fascinating to study.

From zero to $39 billion in three years: the journey

Brett Adcock is not a robotics engineer by training. He is a serial entrepreneur, founder of Vettery, a tech employment marketplace sold to Adecco, and Archer Aviation, an electric air taxi company. Two very different sectors, but one constant: long-cycle, capital-intensive markets where the race for resources matters above everything else.

In 2022, he launched Figure AI with a simple and radical conviction: the global labor market is short of hands. Physical, repetitive and demanding tasks handling, logistics, industrial assembly are becoming increasingly difficult to fill in developed countries. A humanoid robot capable of performing these tasks without redesigning existing production lines would represent a market opportunity potentially larger than the automotive industry itself.

The bet quickly attracted some of the biggest names in tech. In 2024, Figure closed a $675 million Series B round at a valuation of $2.6 billion, with Microsoft, NVIDIA, the OpenAI Startup Fund, Jeff Bezos through Bezos Expeditions, Intel Capital and ARK Invest. The list of investors is almost as impressive as the machines themselves. In September 2025, an additional $1 billion Series C round brought the valuation to $39 billion, with Brookfield Asset Management and Macquarie Capital joining the table alternative asset managers that do not invest in science fiction.

In eighteen months, the valuation increased fifteenfold. That is rarely seen, even in tech. A new funding round is reportedly being prepared for 2026–2027.

Figure 01, Figure 02, Figure 03: three generations, one philosophy

Figure’s product trajectory is fast and methodical, which sets it apart from many robotics startups that produce demo after demo without ever leaving the laboratory.

Figure 01, was the founding prototype, presented in 2022. A bipedal robot measuring 1.67 meters and weighing 60 kilos, capable of walking and grasping objects. Not enough for a real production line, but enough to raise funds and demonstrate a direction.

Figure 02, launched in August 2024, represents the company’s first real industrial deployment. This was the robot sent to BMW’s assembly plant in Spartanburg, South Carolina and that is not a minor detail. For eleven months, Figure 02 worked ten-hour shifts, Monday to Friday, on a clearly defined task: loading sheet metal parts onto welding fixtures. In total, it completed more than 1,250 hours of real operation, loaded more than 90,000 parts, and reportedly contributed to the production of more than 30,000 BMW X3 vehicles.

That is the number that changes everything. You can manufacture beautiful laboratory videos. You cannot so easily manufacture 30,000 cars.

Figure 03, launched in October 2025, marks a philosophical turning point. The third generation is designed for two environments at once: industry, of course, but also the home. Soft exterior covering for safer human contact, wireless charging, redesigned audio interface, 44 degrees of freedom, tactile sensors capable of detecting forces from as little as three grams precise enough to handle an egg without breaking it.

On the hardware side, the wrist architecture was completely redesigned after BMW’s feedback: the motor controllers now communicate directly with the main computer, without an intermediate distribution board, improving reliability and simplifying thermal management. These are the details that distinguish an engineering robot from an industrial robot.

In March 2026, Figure released a video of Figure 03 autonomously tidying up a living room picking up toys, putting cushions back in place, wiping a table, restoring order. The movements are not fast. But they are continuous, natural, and the robot adapts to the changing layout of objects. This is no longer just a laboratory demonstration.

Helix is based on a logic close
to generative AI models: learning
massively through experience

Helix: the brain that makes all the difference

Figure’s hardware is solid. But what truly interests engineers, investors and competitors is Helix the AI model developed entirely in-house, which forms the central nervous system of all Figure robots.

Helix is a Vision-Language-Action model, or VLA. This type of architecture is at the heart of the new generation of intelligent robotics. In practical terms, the robot receives images from its cameras and instructions in natural language, and the model directly produces motor commands. No multi-step pipeline, no spatial segmentation followed by trajectory planning and then execution everything is integrated into a single neural network that sees, understands and acts.

What makes Helix particularly remarkable is that it runs onboard, without relying on the cloud. Figure 03 carries up to two NVIDIA GPUs per unit. Processing is local, eliminating the latency and connectivity issues that would penalize an industrial robot in a real environment.

Helix 02, presented in January 2026, extends this control to the robot’s entire body walking, manipulation, balance all driven by a single unified neural network. The living room demonstration is the direct consequence of this: the robot is not following a script. It observes its environment, identifies objects, plans and executes actions in real time.

This is what Brett Adcock calls the shift from the “programmed” robot to the “learning” robot. The difference is not cosmetic it is architectural. A programmed robot will only ever execute what it has been told to do. A robot driven by a sufficiently trained VLA can, in theory, approach a new task without reprogramming.

The challenge of scaling production: BotQ and Project Go-Big

Developing a humanoid robot capable of working in a factory is one thing. Manufacturing thousands then millions is another matter entirely. This is precisely where many robotics startups hit the wall: industrialization.

Figure anticipated this problem by building BotQ, its dedicated humanoid robot manufacturing facility. The production philosophy changed fundamentally between Figure 02 and Figure 03. While the previous generation relied on CNC machining for most mechanical parts precise but expensive Figure 03 is designed for die casting, plastic injection molding and stamping. These processes require heavier upfront investment, but they reduce unit costs as volumes increase. This is classic industrial logic: invest heavily in capex to reduce variable costs.

At the same time, “Project Go-Big” is a large-scale data collection initiative designed to feed Helix’s training. The more robots operate in real-world conditions, the more the model learns from varied situations, and the more capable it becomes. Field data is the scarce resource in humanoid robotics and Figure has understood that producing it at scale is a durable competitive advantage.

The real challenge in humanoid
robotics is no longer creating a
functional robot, but producing
it at scale

What Figure has not yet proven

An honest analysis of Figure AI must name the grey areas and they do exist.

The $39 billion valuation is built on potential, not on revenue. Figure’s current revenue remains limited. The BMW deployment is a pilot, not a mass-production contract. BMW and Figure are still evaluating additional use cases for Figure 03, and BMW’s March 2026 press release remains cautious about future commitments.

In June 2025, Brett Adcock declined to do a live demonstration at a major tech conference and refused to publicly answer detailed questions about the reality of the BMW partnership. This is not an alarming signal in itself startup CEOs often avoid uncontrolled demos but it is a useful reminder that the distance between a carefully controlled video and a stable industrial production line remains real.

The comparison with Tesla Optimus is inevitable and deserves to be taken seriously. Tesla is targeting a price range of $20,000 to $30,000 per unit, with ambitions to produce millions of units at scale. The Fremont factory, repurposed for Optimus, is already targeting 12,000 units per year in its initial phase. Figure has not announced a public price for Figure 03, nor a short-term production volume target. On this commercial and industrial front, Tesla has strengths that Figure has not yet demonstrated.

Chinese competition also deserves a mention one that too few Western articles take seriously. Around 90% of humanoid robots sold worldwide in 2025 were designed by Chinese companies, according to the International Federation of Robotics. UBTech, Unitree, Fourier Intelligence and XPeng operate with structurally different production costs. The price war in humanoid robots has not yet begun but it will come.

Who is the CEO of Figure AI?

Brett Adcock is an American entrepreneur specialized in breakthrough technologies and artificial intelligence. Before launching Figure AI in 2022, he had already co-founded Archer Aviation, a company developing electric flying taxis.

With Figure AI, Brett Adcock aims to create humanoid robots capable of working in factories, logistics warehouses and, eventually, homes. His stated goal is to develop a new generation of general-purpose robots capable of performing repetitive, physical or dangerous human tasks using artificial intelligence.

In just a few years, Figure AI has become one of the most closely watched startups in the humanoid robotics sector. The company is working on robots capable of sorting parcels, handling objects, cleaning spaces and assisting industrial operators. Several recent demonstrations have attracted millions of views online, particularly videos showing robots completing full autonomous logistics sorting shifts.

For many analysts, Figure AI is now among the most advanced companies in the global race for humanoid robots, alongside Tesla with Optimus and Agility Robotics.

What Figure AI says about the state of the sector

Figure AI is not an isolated story. It is the sign of a specific moment in the history of robotics.

For the first time, several technologies are maturing simultaneously: language models powerful enough to understand complex instructions, onboard GPUs compact enough to fit inside a robot torso, force and vision sensors precise enough for fine manipulation, and supply chains mature enough to make scaled production imaginable.

This was not true in 2015. It was not really true in 2020 either. In 2025–2026, the conditions are coming together for humanoid robots to move from the laboratory to the factory. Figure AI is one of the companies making that transition tangible.

For industrial leaders reading these lines, the question is not yet: “Should I buy a Figure robot?” It is: “Should I follow this technology closely, understand its real capabilities, and prepare for possible integration over the next three to five years?”

The answer to that question is yes with the caution required for any long-term bet on a technology still under development, but also with the attention that a true paradigm shift deserves when it begins to happen.

Robot Magazine follows the evolution of industrial humanoid robotics. Figure AI, Optimus, Boston Dynamics Atlas: the field is taking shape quickly. Next article in this series: the technical showdown of industrial humanoids in 2026.

Cet article The startup that wants to put a humanoid in every factory and maybe in every home est apparu en premier sur Robot Magazine.

As humanoid robotics attracts increasing attention worldwide, a growing number of startups are exploring new real-world applications for these machines. While many companies aim to build general-purpose humanoids capable of performing multiple tasks, TeknTrash Robotics is taking a different approach.

The company focuses on a very specific mission: automating waste handling through humanoid robots.

Robot Magazine spoke with Al Costa, entrepreneur and robotics advocate, about the company’s vision, the evolution of humanoid systems, and why specialization could be the key to making robots truly useful in everyday environments.

Introducing TeknTrash

Robot Magazine: To start, could you briefly explain what TeknTrash does, the problem your company addresses, and the vision that drives your work in robotics and automation?

Al Costa:
TeknTrash was created based on a simple observation: waste handling  both in industrial environments and in homes  is dangerous, unsanitary and unhealthy. It is a task that ideally humans should not have to perform.

To solve this problem, we developed ALPHA, the Automated Litter Processing Humanoid Assistant. ALPHA is a humanoid robot specifically designed to handle waste in many environments.

Our vision differs from many robotics companies currently developing humanoids. The idea of creating general-purpose humanoids capable of performing any task is, in my opinion, unrealistic.

When a company hires someone, it does not expect that person to code software, manage finances, serve coffee and answer customer calls simultaneously. Each job requires a different set of skills.

Biology teaches us the same lesson: specialization matters. Muscles designed for strength cannot perform precision tasks efficiently, and precision muscles cannot deliver high force.

For that reason, TeknTrash focuses entirely on waste handling. This specialization is the foundation of our strategy.

“Robotics should follow the same logic as human work: specialization creates efficiency.”
Al Costa, CEO of TeknTrash Robotics

Humanoid robotics and real-world applications

Robot Magazine: Humanoid robots are attracting strong attention across the robotics industry. How do you see these systems evolving from experimental platforms into real-world solutions?

Al Costa:
My background combines biology and computer engineering, which allows me to approach robotics from both scientific and technological perspectives.

Two years ago, I suggested to an investor that robots could be used during concerts as part of the entertainment experience. At the time, he dismissed the idea, arguing that robots would “sell themselves”.

But recently we saw a major pop concert in China where four humanoid robots danced alongside the singer. The audience loved it.

This example illustrates a broader point: China is currently far ahead in experimenting with real-world uses of robotics. If Western companies want to remain competitive, we need to closely observe what is happening there.

At TeknTrash we opened an office in Suzhou, and I may personally relocate to China in the future.

“China is moving extremely fast in robotics
adoption. Watching what happens there is essential.”
Al Costa

Systems thinking in robotics

Robot Magazine: Modern robotics increasingly relies on integrated systems combining AI, sensors and connectivity. How does TeknTrash approach this challenge?

Al Costa:
One of the biggest limitations for robotics today is computing power.

Human brains are made of carbon atoms. Carbon has the same four chemical bonds as silicon which is used in computer chips but carbon atoms are much smaller. This allows biological brains to pack incredible computing capacity into a very small space.

As a result, the human brain still far surpasses any computer we can currently build.

At the same time, semiconductor manufacturing is approaching atomic limits. Chips cannot keep shrinking indefinitely.

Because of this, robotics will increasingly rely on cloud computing.

Many advanced robotic functions will not run directly on the robot itself but on remote servers performing heavy computations.

This means robots must be connected continuously to powerful computing infrastructure.

TeknTrash is part of the One6G Association, exploring future connectivity technologies. Our architecture has also been recognized by Google Cloud as an example of cloud-based robotics.

“The future of robotics will rely heavily
on cloud intelligence rather than onboard
computing.”

Automation, intelligence and the future of work

Robot Magazine: Beyond productivity gains, how will intelligent automation reshape industries and society?

Al Costa:
Recently a well-known billionaire suggested that robots could eliminate the need for human work within five years.

That reminded me of the film WALL-E, where robots perform every task for humans and society becomes completely dependent on machines.

While that scenario may sound extreme, technological progress is clearly accelerating.

The futurist Ray Kurzweil described this phenomenon with the concept of the technological singularity  a point where technological evolution becomes so rapid that predicting the future becomes extremely difficult.

Whether or not we reach that point soon, intelligent automation will undoubtedly transform many industries in the coming decades.

Leadership and the future of robotics

Robot Magazine: What mindset shift is required to accelerate the adoption of humanoid robots?

Al Costa:
Technological pessimism has always existed.

When the telephone was invented, many people believed it was unnecessary because the telegraph already worked perfectly.

Innovation often faces resistance because people cannot imagine how new technologies will change everyday life.

Henry Ford famously said that if he had asked customers what they wanted, they would have answered: “a faster horse”.

Today many societies face labor shortages due to demographic changes. In Japan, for example, more adult diapers are sold than baby diapers.

This shows that robots will increasingly be needed to supplement human labor.

Our responsibility as technologists is to develop tools that improve society, even when people initially struggle to imagine their value.

About Al Costa

Al Costa is CEO of TeknTrash Robotics, a company focused on developing humanoid robots dedicated to waste handling in industrial and residential environments.

A serial entrepreneur, he has founded companies in the United States, the United Kingdom, Spain, China and Brazil. His first startup was acquired by a company listed on NASDAQ.

He is also the author of five books, including a science-fiction novel, and previously taught Big Data and Machine Learning at a university in Spain.

Having grown up in several countries as the son of a diplomat, he is also the founder of an NGO dedicated to protecting children in war zones.

FAQ – Humanoid Robots for Waste Management and the Vision of TeknTrash

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These three building blocks redefine what a robot can understand, perceive, and execute in the real world. They mark the shift from programmed, rigid robotics to cognitive robotics, capable of operating in uncertain environments. For industry, this represents a major structural change.

Embodied AI: Where Intelligence Meets Physics

For a long time, artificial intelligence remained separate from the physical world. AI models excelled in data analysis, image recognition, or language processing, but they did not interact directly with matter.

Embodied AI changes this equation. It refers to systems where intelligence is integrated into a physical body capable of acting, experimenting, and learning through interaction. The robot no longer merely executes instructions; it adapts its actions based on sensory feedback.

Platforms like those developed by NVIDIA, with their advanced simulation environments, or the integrated robotic architectures offered by Tesla and Boston Dynamics, illustrate this evolution. AI is no longer an external module: it is embedded, connected to sensors, motors, and actuators.

This embodiment enables learning through real or simulated interaction. The robot can test strategies, correct its mistakes, and optimize its trajectories. Intelligence becomes dynamic, contextual, and adaptive.

3D Vision: Understanding Space Rather Than Mapping It

Traditional robotic vision relied on controlled environments: floor markings, standardized parts, calibrated lighting. Systems were effective but poorly tolerant of variation.

Next-generation 3D vision disrupts this approach. Thanks to stereoscopic cameras, depth sensors, miniaturized LiDARs, and spatial reconstruction algorithms, robots can now interpret complex environments in real time.

They no longer just recognize a part; they evaluate its exact position, orientation, dimensions, and interaction with other objects. This capability is crucial for:

• Automated logistics

• Flexible assembly

• Handling non-standard objects

• Safe human interaction

3D vision allows robots to move from a predefined world to a partially unpredictable one. It reduces reliance on rigid infrastructure and paves the way for more flexible automation.

Intelligence is no longer external
to the robot: it is integrated, sensory,
and adaptive.

Fine Manipulation: The Most Complex Frontier

While locomotion has long been seen as the main robotic challenge, fine manipulation is actually one of the hardest obstacles to overcome.

Grasping a fragile object, screwing a component, handling a cable, or adjusting a flexible part requires subtle coordination between perception, computation, and motor control. Human hands have thousands of biological sensors and unmatched dexterity.

New generations of robots integrate high-precision force sensors, multi-jointed grippers, and algorithms capable of adjusting pressure in real time. Manipulation no longer relies solely on a predefined trajectory but on continuous adaptation.

This technological breakthrough makes it possible to automate tasks previously reserved for skilled operators: delicate assembly, complex sorting, light maintenance, and preparing varied orders.

The Convergence of the Three Breakthroughs

It is the combination of embodied AI, 3D vision, and fine manipulation that creates the true breakthrough. Individually, each represents an advancement. Together, they transform the very nature of the robot.

A robot with 3D vision but no adaptive intelligence remains limited. A powerful AI without reliable perception is blind. A skilled hand without contextual understanding lacks relevance. The convergence of these building blocks creates systems capable of:

• Perceiving an unstructured environment

• Choosing an appropriate strategy

• Executing precise actions while accounting for sensory feedback

This synergy forms the core of next-generation robotics.

Impact on the Factory of the Future

In industrial contexts, these breakthroughs allow us to move beyond rigid, ultra-specialized lines. Robots become capable of handling shorter runs, customized products, and variable flows.

The factory evolves toward a modular and adaptive model. Intelligent robots can be reconfigured more quickly, learn new tasks through demonstration, and cooperate with human operators.

This flexibility directly addresses contemporary challenges: market volatility, skill shortages, higher quality demands, and cost pressures.

Still Structuring Limits

Despite these advances, several challenges remain. System robustness in highly disrupted environments still needs consolidation. Learning in real conditions requires strict safeguards to avoid unforeseen behaviors.

Cybersecurity is also a major concern: a connected, intelligent robot represents a potential attack surface. Certification of systems integrating adaptive AI raises complex regulatory questions.

Technological breakthroughs do not mean immediate maturity. They indicate an irreversible trajectory.

A Structured Global Competition

The United States, Europe, and Asia are investing heavily in these technologies. Advanced simulation platforms, specialized processors for embedded AI, and cloud infrastructures dedicated to robotics accelerate development.

The race is no longer only about raw performance but about the ability to industrialize these innovations at scale. Success will depend as much on software mastery as on production capability.

AI, perception, and manipulation:
alone, they advance; together, they
revolutionize robotics.

Toward Widespread Cognitive Robotics

Embodied AI, 3D vision, and fine manipulation are not merely incremental evolutions. They mark the shift toward cognitive robotics, capable of operating in dynamic environments.

This transformation does not mean robots will become autonomous in the human sense. It means they will be able to perform varied tasks with minimal but sufficient contextual understanding to act effectively.

Industry, logistics, healthcare, and services will be gradually impacted.

The True Breakthroughs Are Invisible

The most decisive advances in robotics are not always visually spectacular. They lie in invisible technological layers: learning algorithms, high-precision sensors, real-time control architectures.

Embodied AI gives robots adaptive capacity. 3D vision provides fine spatial understanding. Precise manipulation allows credible interaction with matter.

Together, these breakthroughs redefine the realm of possibility. They do not promise total automation but smarter, more flexible, and more integrated automation.

Robotics is entering a new phase: one where performance is measured not only in speed or strength, but in the ability to understand and act in the real world.

FAQ – Embodied AI, 3D Vision, and Fine Manipulation: Key Robotic Breakthroughs

Cet article Embodied AI, 3D Vision, Fine Manipulation: The true Robotic breakthroughs est apparu en premier sur Robot Magazine.

Unlike pure software, robotics does not develop solely in a garage or behind a screen. It requires a rare convergence of skills: mechanics, electronics, artificial intelligence, industrial production, and field validation. And in this precise area, Zurich has major structural advantages.

Robotics Does Not Follow Software Rules

Most robotics startups do not fail because their idea is bad. They often fail because they are located in the wrong ecosystem.

Creating a robot is not just about writing code. It requires:

• Integrating hardware and software end-to-end

• Physically testing prototypes

• Accessing pilot factories

• Enduring long R&D cycles

• Raising patient capital

These constraints are exactly what differentiate robotics from a typical SaaS startup. And this is where Zurich makes a difference.

ETH Zurich: A Deep-Tech Talent Factory

At the heart of the ecosystem is ETH Zurich, consistently ranked among the world’s top technical universities.

Each year, ETH feeds the local ecosystem with engineers specialized in:

• Advanced robotics

• Mechatronics

• Artificial intelligence

• Computer vision

• Embedded systems

Beyond the degree, it is the “engineering-first” culture that makes the difference. Professionals from Zurich know how to design, prototype, and industrialize.

Influential research centers, such as Disney Research and the RAI Institute, also orbit ETH, maintaining a constant flow of innovation and scientific publications.

In robotics, geography does not
just determine opportunity; it
determines the speed of innovation.

Industrial Validation: A Key Advantage

Switzerland, and particularly the Zurich region, benefits from a strong industrial base: automation, microtechnology, machine tools, medical devices.

For a robotics startup, this means:

• Rapid access to industrial partners

• Opportunities to test prototypes in real conditions

• Early B2B clients ready to experiment

Robotics cannot remain confined to a lab. It must face real-world constraints: dust, vibrations, deadlines, maintenance. Zurich offers this critical proximity between research and industry.

Patient Capital for Long Cycles

Another decisive factor is financing.

Robotics does not progress in a straight line. Cycles are longer, pivots are costlier, hardware iterations more complex. The Swiss ecosystem has historically been structured around deep-tech and hardware, with:

• Specialized investors

• Public funds

• Grant programs adapted to industrial risks

Unlike ecosystems focused on rapid growth and accelerated exits, Zurich accepts the timescale inherent to robotics.

An Impressive Concentration of Robotics Startups

The region already hosts a remarkable density of players, including:

• ANYbotics

• Gravis Robotics

• Verity

• Voliro

• Sevensense

• Wingtra

• Auterion

• Embotech

• Ascento

Additionally, student initiatives like ETH Robotics Club actively contribute to the local entrepreneurial culture.

This concentration creates a network effect: mobile talent, shared know-how, specialized subcontractors, investors familiar with hardware.

What’s Still Missing: A More Collaborative Startup Culture

While Zurich checks nearly all the boxes talent, research, industry, capital one point remains to be perfected: a supportive culture among founders.

In San Francisco, entrepreneurs easily share mistakes, suppliers, and investors. This “shared context” accelerates collective learning.

Zurich has undeniable technical excellence. The challenge now is to intensify community dynamics among founders to create a true Silicon Valley-style deep-tech effect.

Some initiatives are already emerging, notably in the Schlieren district, where several startups and incubators cluster to promote informal exchanges and collaborative work.

ETH Zurich fuels the ecosystem
with deep-tech talent trained in
robotics, mechatronics, and artificial
intelligence.

Why Zurich is Becoming Strategic for Europe

For European entrepreneurs, Zurich now represents a credible alternative to the United States and Asia.

In a context where:

• Industrial robotics is accelerating

• AI is integrating with hardware

• Technological sovereignty is becoming a major issue

Switzerland offers a neutral, stable, and highly skilled ground.

For the European robotics industry, being present in Zurich is no longer just an academic opportunity; it is a strategic choice for location.

A Broader Lesson for the Robotics Ecosystem

The message is clear: in robotics, geography matters.

A poor ecosystem slows innovation. A good ecosystem accelerates it.

Zurich demonstrates that a deep-tech hub is not built solely around venture capital but around a subtle balance of:

• Scientific excellence

• Industrial anchoring

• Appropriate financing

• Entrepreneurial community

For founders building robots not just apps the question may no longer be “where to raise funds?” but “where to build sustainably?”

And in this precise regard, Zurich is emerging as one of the strategic nodes of global robotics.

FAQ – Zurich, Strategic Hub for European Robotics

Cet article Zurich: The Discreet Ecosystem Driving European Robotics est apparu en premier sur Robot Magazine.