Wandelbots Blog

Commentary: Bullhound's Robotics Report Gets the Market Right. The Missing Piece Is the Operating Model.

Wandelbots — Wed, 29 Jul 2026 22:00:00 GMT

Some reports lose relevance within weeks. Bullhound Capital's Assembled Intelligence – The Layered Investment Case for Robotics, published in May 2026, has done the opposite.

As the industry continues to debate AI, humanoids, and automation, its structural analysis has only become more relevant. Rather than evaluating individual vendors or technologies, the report examines the structural forces reshaping the industry: labour scarcity, deployment experience, production reliability, operational infrastructure, and the changing balance between China, the United States, and Europe. Collectively, these trends point to a fundamental shift in where competitive advantage is created. Increasingly, lasting value will belong to the companies that can deploy, operate, and continuously improve robotics at industrial scale. Therefore, companies must build an operating model capable of turning that technology into an enterprise capability. So, what exactly does that mean for manufacturing companies, and how are current market changes affecting this operating model?

The deployment moat is organizational

Labour scarcity is no longer a temporary market imbalance. Skilled workers are retiring faster than companies can replace them, while decades of production knowledge disappear with them. Bullhound rightly describes this as a structural demand floor for robotics rather than another automation cycle. The implication reaches further than investment in robots. Companies need a way to retain, reuse, and continuously improve operational knowledge instead of rebuilding every project at every new site.

Bullhound's second insight is equally important. The deployment moat is not built through intellectual property alone. It is built through production hours, recovery loops, and experience under real operating conditions. That changes how manufacturers should think about deployment. A successful Proof of Value is important, but it does not create a competitive advantage on its own. Competitive advantage emerges when every deployment strengthens the next one, when operational learning becomes reusable instead of remaining local to one factory or one engineering team.

When automation logic, governance, digital twins, and operational learning can be applied repeatedly across sites, every implementation becomes faster, less risky, and less dependent on scarce engineering expertise. The value compounds because the organization compounds. That is fundamentally different from delivering a series of successful automation projects.

The next competitive advantage is the operating model

Many manufacturers still treat automation as a string of one-off engineering projects: a business case gets approved, a cell gets automated, the project is commissioned, and the team moves on to the next request. Each deployment starts over, with different stakeholders, different assumptions, and often different technology. That operating model worked when automation was primarily about individual machines. It becomes increasingly expensive when automation is expected to improve continuously across multiple production lines, factories, and regions.

The companies that lead Physical AI will not simply deploy different technology. They will build a different operating model.

They will move from use case thinking to platform thinking, where automation capabilities become reusable standards across sites instead of remaining tied to individual projects. They will replace project delivery with lifecycle operations, where simulation, deployment, monitoring, optimization, and reuse become one continuous process. Ownership will extend beyond automation engineering to include IT, operations, and business leadership because competitiveness increasingly depends on all three making decisions together. Manual engineering will gradually give way to software-defined workflows that can be governed, validated, and improved over time. Most importantly, pilots will no longer be treated as isolated proofs of concept. They will become the first implementation of an enterprise standard.

Bullhound's argument that reliability matters more than capability reinforces exactly this point. In manufacturing, benchmark performance does not create value unless it can be delivered consistently under production conditions. Reliability depends as much on safety, governance, standardized processes, and organizational discipline as it does on intelligence itself. Only if a company “can convert technical capability into certified, recurring deployment”, they will be able to successfully scale their production in the future.

Competitive advantage compounds through standards

Bullhound argues that the most durable economics in robotics will belong to operational infrastructure rather than technology vendors. Operational infrastructure is not only software. It is the combination of governance, reusable workflows, lifecycle management, and enterprise standards that allows software to create value repeatedly instead of once. Without that operating model, even the most capable technology remains another successful pilot.

The report's final observation points to the same conclusion. While China, the United States, and Europe each contribute different strengths to the robotics ecosystem, hardware supply chains, software infrastructure, embedded systems, and deployment expertise will remain globally interconnected rather than converge into a single dominant stack. Manufacturers therefore need an operating model that can evolve with that ecosystem instead of becoming dependent on one technology path. Vendor-agnostic and hardware-agnostic infrastructure becomes a strategic business decision because it preserves optionality while allowing automation standards to scale across the enterprise.

This is also where software-defined automation becomes a strategic business decision rather than a technology discussion. For years, manufacturers have optimized individual automation projects. However, we are convinced that software increasingly becomes the layer that defines how automation is deployed, governed, adapted, and continuously improved across the enterprise. The competitive advantage no longer comes from programming one robot more efficiently. It comes from creating a reusable execution model that allows hundreds of robots, production lines, and sites to evolve without starting over.

That requires an infrastructure layer that sits above individual hardware and vendor ecosystems. A vendor-agnostic and hardware-agnostic platform that allows manufacturers to standardize how automation is built and operated while preserving the flexibility to integrate different robots, applications, and future AI capabilities. This is the foundation of software-defined automation.

Wandelbots NOVA was designed around exactly this shift. Rather than treating automation as a sequence of isolated engineering projects, NOVA provides the infrastructure layer that connects AI, digital twins, and robot execution so manufacturers can build reusable standards instead of repeatedly rebuilding individual solutions. The objective is not to replace existing automation investments, but to make them scalable, governable, and continuously improvable by adding the missing execution layer across existing automation landscapes. This is what Wandelbots means by Physical AI: not intelligence applied to a single robot, but intelligence that governs physical operations across an enterprise, continuously learning from production, standardizing execution, and improving outcomes across sites. The companies that build this capability first will not simply deploy more automation. They will establish an operating model where every deployment strengthens the next, every site contributes to a shared standard, and every improvement compounds across the enterprise, creating a competitive advantage that becomes increasingly difficult to replicate.

Prepare Your Organization for What's Next

The Executive Integration Playbook provides a structured framework to assess your organization's operating model across four phases – from strategic alignment and the first Proof of Value to enterprise-wide deployment and continuous optimization. It helps manufacturing leaders understand where they stand today and which organizational capabilities must be established before automation can scale across the business.

Humanoids Are a Bridge. The Destination Is Intelligent Automation.

Shawn Cowdrey — Sun, 28 Jun 2026 22:00:00 GMT

The most important question in robotics isn't who builds the best humanoid. It's who builds the intelligence layer that powers physical work.

Humanoid robots are attracting attention because they make the next phase of automation easy to imagine, but their real importance is not that they look like people. Their importance is that they represent a broader shift towards intelligent automation adoption: systems that can understand, decide, and act in the physical world with more adaptability than traditional automation. The humanoid form factor may become useful in some environments, especially those already built around human movement and human work, but the bigger story is not the body. The bigger story is the intelligence layer that will eventually move across many different types of robots, machines, and industrial processes.

I have been around manufacturing long enough to have seen a few technology waves arrive with enormous promise. When I started working in and around industrial automation, Industry 4.0 and digitisation were the dominant ideas. Every factory was going to become smarter. Every machine would be connected. Every process would generate data. Dashboards, digital twins, predictive maintenance, cloud platforms, and connected production systems were all presented as part of a more intelligent industrial future.

A lot of that progress was real. Manufacturing did become more connected, more measurable, and in many cases more efficient. But it also taught the industry an important lesson: technology does not automatically solve the underlying complexity of production. If a process is poorly understood, inconsistently managed, or full of hidden edge cases, digitising it does not magically make it simple. Sometimes it simply makes the complexity easier to see.

We are now entering a similar moment with AI. The language has changed, but the ambition feels familiar. We are again talking about flexible systems, adaptive automation, intelligent decision-making, and software that can reduce the burden of engineering every detail in advance. This time, the promise is not only that machines will be connected, but that they will be able to understand, reason, and act.

That is a powerful idea, and I believe there is substance behind it. But it also deserves some caution.

In manufacturing, “flexibility” has always been one of the most attractive words in automation. It suggests that a system can handle variation, absorb uncertainty, and continue working even when the real world refuses to behave exactly as expected. The challenge is that flexibility can also become a way of postponing difficult process decisions. Rather than fully defining the edge cases, stabilising the workflow, or addressing the operational reasons why variation exists in the first place, we sometimes hope that a more flexible technology layer will absorb the messiness for us.

I saw a version of this many times with industrial cameras before the current AI era. Vision systems were often treated as a universal answer because they felt flexible. A camera seemed to offer the possibility of detecting almost anything, especially in situations where the correct sensor was difficult to define. In practice, many of those applications could have been solved with simpler, cheaper, and more reliable solutions: a basic sensor, a mechanical guide, a fixture, a light barrier, or a small change to the process. But customers were often willing to pay a premium for the expectation of flexibility, even when that flexibility created more complexity than value.

That experience shapes how I look at humanoid robots today.

Humanoids are compelling because they represent flexibility in its most visible form. They look like they should be able to step into the human-built world and deal with its complexity. They suggest that instead of redesigning a process around automation, we might one day deploy automation into the process as it already exists.

That is why the current interest in humanoids matters. It is also why we need to be careful about what we are really looking at.

Humanoid robots will not succeed simply because they are the best robots. In many manufacturing environments, they are not. Industrial robot arms are faster, more precise, more robust, and easier to justify when the task is clearly defined. Autonomous mobile robots are usually better suited to moving goods. Purpose-built automation remains the obvious choice when a process is repetitive, stable, and economically worth optimising.

So when manufacturers pay attention to humanoids, I do not think the important question is whether humanoids are better than industrial robots. Most of the time, they are solving a different problem. The real question is whether we are entering a new phase of intelligent automation adoption where adaptability becomes valuable enough to justify a very different kind of machine.

Why Humanoids Are Back in the Conversation

Humanoid robots have been a long-standing ambition in robotics, but for decades they lived mostly in research labs, technology demonstrations, and science fiction. They were impressive to watch, but difficult to imagine as reliable industrial tools. The gap between a controlled demo and a real production environment was simply too large.

That is starting to change.

Major automotive manufacturers are now testing humanoids in real production environments. BMW has piloted Figure humanoid robots at its Spartanburg plant, including work related to sheet-metal handling in the production process. Mercedes-Benz has also announced work with Apptronik to explore Apollo humanoid robots in manufacturing logistics, including bringing parts to production lines and inspecting components. [1][2]

These examples should not be overinterpreted. They do not mean humanoids are ready to replace industrial automation, and they do not mean factories will soon be full of general-purpose robotic workers. What they do show is that manufacturers are beginning to explore a different category of automation. This category is less about optimising a single task and more about adapting across many tasks in environments that already exist.

That distinction matters because traditional automation works best when the environment is engineered around the machine. Fixtures, conveyors, safety systems, work cells, tooling, and process flows are designed so that the robot can repeat a specific task with high reliability. This is an extremely powerful model, and it will remain central to manufacturing.

But it also has limits. Not every process is stable enough. Not every factory can be rebuilt. Not every use case justifies a dedicated automation project. Humanoids suggest a different possibility. Instead of asking manufacturers to redesign the environment around the robot, they point towards robots that can operate in environments already designed for people.

That is where the opportunity begins.

The Uncomfortable Truth: Humanoids Are Often Worse Robots

The current hype around humanoids can make it sound as though the human body is the ideal design for manufacturing. It is not.

If the job is to weld a car body, pick components from a known position, move material from A to B, or palletise boxes at high speed, there are usually better machine designs available. A humanoid brings a level of complexity that many industrial robots avoid entirely. It must balance, perceive, navigate, manipulate, recover from uncertainty, and operate safely around people. Every one of those capabilities adds cost, risk, and engineering effort.

This is why I do not think the future of humanoids should be framed as a replacement story. Industrial robots are not going away. Fixed automation is not going away. Purpose-built machines are not going away. In the places where speed, precision, repeatability, and throughput are the main drivers, specialised automation will continue to win.

The more interesting space is where adaptability creates more value than efficiency.

Many industrial environments are not perfectly optimised. Tasks change. Products vary. Workstations evolve. Labour availability shifts. Infrastructure ages. The surrounding process may not be stable enough to justify a dedicated automation solution, even if the task itself looks simple from the outside.

That is where humanoids become interesting. They may not be the best machine for any single task, but if they can be redeployed across many tasks, the business case starts to change. This is especially relevant for manufacturers exploring robots-as-a-service models, where the value of a robot is not only measured by the efficiency of one process, but by how effectively it can be reused over time.

In that sense, the economic promise of humanoids is not maximum performance. It is optionality.

The Factory Was Built for Humans

Manufacturing environments are full of human assumptions.

Tools are placed at human height. Workstations are designed around human reach. Doors, handles, carts, bins, stairways, inspection points, and maintenance areas all reflect the fact that people have been the default unit of physical work for more than a century.

Many processes remain manual not because manufacturers prefer manual work, but because automating them would require redesigning too much of the surrounding environment. A fixed robot cell might solve the task, but only after changes to layout, safety systems, tooling, fixtures, software integration, and process flow. For high-volume, stable production, that investment can make sense. For variable work, it often does not.

This is one of the strongest practical arguments for humanoids. They offer a possible path to automation without rebuilding everything around automation.

A humanoid can, in theory, move through the same spaces as a person, use similar tools, interact with existing workflows, and perform tasks in areas where fixed automation would be too expensive, too disruptive, or too inflexible.

That does not make humanoids universally better. It makes them strategically different. Their value is that they are compatible with a world built for people.

The Generalist Trap

A lot of attention in humanoid robotics is now focused on fully autonomous humanoids that can reason, understand the world, and perform tasks they have never seen before. This is understandable. It is an exciting vision, and it fits naturally with the current momentum around large AI models and world models.

But from a manufacturing perspective, it also risks missing something important.

Manufacturing has spent more than a hundred years doing almost the opposite. It has taken broad human capability and focused it into narrower roles, clearer responsibilities, repeatable processes, and specialised skills. The same is true of much of our education and training system. We do not generally train people to do everything. We train them to become useful in particular contexts, with particular tools, constraints, and responsibilities.

That narrowing is not a failure of imagination. It is one of the reasons modern manufacturing works.

A skilled operator, technician, welder, machinist, quality inspector, or maintenance engineer is valuable not because they can do every possible task, but because they can perform a meaningful set of tasks reliably within a specific environment. Their value comes from focused competence, context, judgement, and experience.

This is why the idea of making machines more generalist than humans deserves careful scrutiny. Many of the devices we use in automation today are good at one task, or a small number of tasks, precisely because that focus makes them reliable. Now we are asking new robotic systems to become general-purpose enough to deal with unfamiliar tasks, changing environments, uncertain instructions, and physical edge cases that even people often need training to handle.

There may be a future where robots can do that well, but it is probably not the most practical starting point for manufacturing.

A more realistic path sits somewhere in the middle. Not a fixed machine that can only perform one narrow motion, and not a fully autonomous humanoid expected to reason through any unseen situation like a general worker. The useful middle ground is a machine that is highly capable at a selected set of valuable tasks, within known constraints such as location, available resources, safety requirements, tooling, process variation, and business value.

That is also how intelligent automation adoption is likely to happen in practice.

Companies will not adopt humanoids because they can theoretically do anything. They will adopt them when they can reliably do something valuable, then something else, then a growing set of related tasks that make sense in the same environment. The value will come from building useful capability over time, not from expecting general intelligence on day one.

This applies to people as well as machines. A factory becomes more adaptable either by training people to hold multiple useful skills, by deploying machines that can perform multiple valuable skills, or by combining both in a way that makes the whole system more resilient.

That is the happy medium manufacturers should be looking for.

Acceptance May Matter More Than We Think

There is another reason humanoids are attracting attention, and it is not purely technical.

People respond differently to robots that look and move like people. Humans naturally personify humanoid machines. We instinctively interpret where they are looking, what they might do next, and how they are interacting with the environment. That can make them easier to understand than other forms of automation, even when the underlying technology is more complex.

This matters because technology adoption in manufacturing is never only an engineering problem. It is also a human and organisational problem.

Operators, managers, safety teams, unions, and works councils all influence whether a new technology is accepted. A robot that feels understandable may face less resistance than a machine that feels unfamiliar or opaque, even if the unfamiliar machine is technically more efficient.

At the same time, the humanoid form factor may create new concerns. If a robot looks like a worker and performs worker-like tasks, people may perceive it as a more direct threat to jobs than a conventional machine. The symbolism is stronger, and the emotional reaction may be stronger as well.

That is why acceptance cannot be treated as an afterthought. For humanoids to succeed commercially, manufacturers will need to communicate clearly what these systems are for, where they add value, how they are governed, and how they fit into the human workforce.

Mechanical performance will matter, but trust, perception, and communication may matter just as much.

The Real Story Is Software

The most important question in humanoid robotics is not who builds the best mechanical body. It is who builds the best software layer for physical work.

Recent advances in AI have changed what robots can potentially do. Large language models and vision-language-action models are beginning to connect perception, instruction-following, reasoning, and action. Research systems such as Google DeepMind’s RT-2 have shown how web-scale vision-language learning can be connected to robot control, while NVIDIA’s GR00T work points towards foundation models for humanoid reasoning and skills. [3][4]

This is significant, but it should not be confused with readiness for full autonomy in manufacturing. Factories are demanding environments. They require reliability, safety, repeatability, traceability, and integration with existing systems. A robot that succeeds nine times out of ten may be impressive in a demo, but unacceptable in production. Industrial automation is not judged by whether it works once. It is judged by whether it works predictably, safely, and continuously.

That is why the future of Physical AI may not be one giant model that understands everything. A more realistic path is a system of specialised capabilities coordinated by a reasoning layer.

A humanoid does not need to understand the world in an abstract, human-like sense. It needs to perform useful industrial actions. It needs to load a machine, move a tote, inspect a part, open a door, handle a tool, fetch material, support an operator, or recover from a small process variation.

Each of these capabilities can become a skill. The reasoning layer decides which skill to use, when to use it, and how to respond when the situation changes.

That is a much more practical vision for manufacturing than waiting for artificial general intelligence.

From Humanoid Robots to Intelligent Automation Platforms

This is where the conversation becomes bigger than humanoids.

Humanoids are one embodiment of Physical AI, but they are not the only one. Physical AI means software that can understand, decide, and act in the physical world. That intelligence may be deployed through a humanoid robot, an industrial robot arm, an autonomous mobile robot, a mobile manipulator, a drone, or a machine that does not yet exist.

The long-term opportunity is not to build one perfect robot body. The opportunity is to build platforms that allow intelligence to move across different robot forms, environments, and tasks.

This is why humanoids are so important, but also why they should not be mistaken for the destination. They are a visible entry point into a much larger transformation. They attract attention because they are familiar, dramatic, and easy to understand. But the deeper shift is towards software-defined automation: automation that can be configured, adapted, and improved through software rather than rebuilt from scratch for every new use case.

For manufacturers, that is the strategic point. The future may not be defined by whether a robot has two arms, two legs, or a human-like face. It may be defined by whether physical work can be programmed, adapted, deployed, and orchestrated with the flexibility we increasingly expect from software.

That is why intelligent automation adoption matters more than the humanoid itself. The lasting value will come from making physical work easier to deploy, easier to adapt, easier to govern, and easier to scale across real industrial environments.

Why Europe Has a Role to Play

This shift is especially relevant for Europe.

The DACH region is already one of the strongest industrial automation markets in the world. Germany remains a leading robotics nation, with very high robot density and a major share of Europe’s industrial robot base. [5]

At the same time, European manufacturers are facing a structural labour challenge. The German Chamber of Commerce and Industry reported in its 2025/2026 skilled labour report that 36 percent of surveyed companies were at least partially unable to fill vacancies due to a lack of suitable personnel. [6]

That combination creates pressure to automate, but Europe is unlikely to adopt Physical AI in exactly the same way as every other region. European manufacturers care deeply about quality, safety, reliability, data ownership, and governance. With the EU AI Act creating a risk-based framework for trustworthy AI, and the EU Data Act strengthening access and rules around industrial data, AI in manufacturing will increasingly need to be explainable, controllable, and compatible with European expectations around data sovereignty. [7][8]

This may become a strength rather than a limitation.

Europe does not need to win by building the flashiest humanoid demo. It can win by building trustworthy, high-quality systems for orchestrating physical work in real industrial environments.

The companies that matter most may not be the ones with the most viral robot videos. They may be the ones that make intelligent automation safe, reliable, sovereign, and genuinely useful on the factory floor.

What Happens by 2030?

By 2030, the conversation around humanoids may look very different.

Today, the form factor is the story. It captures attention because it is visible, familiar, and emotionally powerful. But as the technology matures, manufacturers will care less about whether a robot looks human and more about what work it can reliably perform.

The first wave of interest will be driven by the form factor. The second wave will be driven by useful applications. The third wave will be driven by orchestration: how different robots, skills, AI systems, and human operators work together as part of one production environment.

Some of today’s hype will fade. Limitations will become clearer. There will likely be disappointing pilots, overpromised deployments, and use cases where humanoids simply do not make sense.

But that does not mean humanoids will fail. It means the market will become more honest.

Humanoids will not replace every worker, they will not replace industrial robots, and they will not make every factory fully autonomous. Their real contribution may be different: they may accelerate intelligent automation adoption by making the idea of adaptable physical work easier to understand, test, and deploy.

The Humanoid Is a Bridge

I do not think humanoid robots are the destination. I think they are a bridge.

They are a bridge between human-built environments and software-defined automation. They matter because factories were built for people, because people can understand them, and because they create a visible and practical entry point into Physical AI.

The destination is not a robot that looks like us. The destination is intelligent automation that can be deployed into the physical world safely, reliably, and flexibly.

By 2030, manufacturers may not be buying “humanoids” in the way we talk about them today. They may be buying adaptable physical work, delivered through whatever machine makes the most sense for the task, the environment, and the business case.

Physical AI in Action: The Wandelbots Live Demo at Hannover Messe 2026

Naveed Bhuiyan — Tue, 26 May 2026 22:00:00 GMT

At Hannover Messe 2026, Wandelbots presented a live demonstration showing how simulation, AI, orchestration, and physical automation systems can operate within one connected production workflow.

Rather than focusing on a narrowly industry-specific use case, the demonstrator was intentionally designed as an abstract automation scenario applicable across different manufacturing environments.

An AGV delivers randomly positioned cubes into a collection bin, from which the robots can pick them up. A 3D vision system scans the scene, AI identifies cube positions and visible letters, and multiple robots coordinate in real time to assemble randomly selected words such as ADAPT, DATA, ACT, or AI.

The demonstrator showed how AI-based perception, simulation environments, and orchestration software can be integrated into a unified operational workflow spanning both digital and physical production systems.

Embedded in the video below is one complete production cycle recorded live at the booth:

A Continuous Operational Loop Instead of a Static Automation Cell

Traditional automation systems are often designed around fixed assumptions:

predefined positions
predictable workflows
isolated machines
vendor-specific logic
manual intervention during faults or downtime

The demonstrator introduced variability directly into the process.

Cube positions changed continuously.
Word selection changed dynamically.
Gripping situations varied from cycle to cycle.
AGV downtime scenarios became part of the workflow logic.
Multi-vendor hardware operated natively under a unified Python platform via Wandelbots NOVA OS

Instead of treating these situations as exceptions, the system was designed to adapt to them in real time through a continuous operational cycle built around four connected phases.

1. Sense

Robots, sensors, cameras, IPCs, and the AGV continuously stream information into the system. The 3D vision setup detects cube positions and visible letters in real time while AI models classify and interpret the scene before execution begins.

This allows the workcell to react dynamically to physical variability instead of relying on rigid positioning assumptions.

During the demo, the system continuously adapted to:

Randomly scattered cube positions
Changing target words
Varying gripping orientations
Runtime workflow changes
AGV downtime situations

One particularly important scenario demonstrated operational resilience: if the AGV became unavailable, the robots automatically bypassed the AGV workflow and continued operation through direct robot-to-robot handovers without requiring manual intervention.

Instead of stopping the workflow, the orchestration layer rerouted execution dynamically.

2. Think

Before execution happens on the physical system, workflows are simulated and validated in a digital environment.

The complete demonstrator exists as a digital twin in NVIDIA Omniverse, where robot behavior, workflows, and AI-driven processes can be tested before deployment into the live cell.

Vision models are initially trained using synthetic data inside simulation environments and then refined using operational feedback from the real system.

At Hannover Messe, visitors could see this directly: while the physical production process executed on the booth floor, a live simulation of the same workflow ran simultaneously in the background.

The demonstrator combined four connected layers:

The physical automation cell
The digital twin in NVIDIA Omniverse
AI training workflows using synthetic data
Operational orchestration through Wandelbots NOVA

The purpose is to reduce the gap between planning, validation, deployment, and continuous optimization.

3. Act

Once validated, workflows are executed directly through Wandelbots NOVA across heterogeneous automation systems.

In this demonstrator, a single Wandelbots NOVA instance orchestrated:

The GESSbot AGV
Yaskawa robots
KUKA robots
AI-powered vision systems
External IPCs
Runtime workflow execution

Wandelbots NOVA acted as the central orchestration and communication layer connecting all operational components into one coordinated system.

This is especially relevant in environments where manufacturers operate mixed hardware ecosystems instead of relying on a single vendor stack.

AI-based detection itself ran locally on-device to enable low-latency execution, while broader AI pipelines could operate either cloud-based or fully on-premise depending on production requirements.

The result is an architecture where orchestration logic, AI services, simulation environments, and physical execution can remain modular while still operating as one connected production system.

4. Improve

The final phase closes the operational loop.

Because Wandelbots NOVA acts as the unified operational backbone, live troubleshooting and operational maintenance become integrated parts of the production workflow rather than isolated engineering tasks.

Centralized logging and observability allow operators and engineers to:

identify failures quickly
inspect runtime behavior
isolate affected components
restart or manage individual Kubernetes pods when necessary

This modular architecture improves robustness while maintaining centralized orchestration across the entire system.

At the same time, operational data continuously flows back into the system to improve future execution cycles.

Real production feedback helps optimize:

AI detection quality
motion handling
orchestration logic
runtime robustness
operational stability

Importantly, these improvements happen without rebuilding the entire application stack from scratch.

What the Demonstrator Actually Represents

This demo goes beyond showcasing a single robotic task. It illustrates how Physical AI can connect simulation, orchestration, perception, and runtime execution into adaptable production environments where:

Applications become portable across hardware environments
Simulation and production stay continuously connected
AI models improve through operational feedback loops
Orchestration becomes hardware-agnostic
Production systems become adaptable instead of static

The result is an automation approach that can scale beyond isolated demo cells into repeatable operational frameworks for real manufacturing environments.

Thank You to Our Partners

This demonstrator was made possible through collaboration with multiple partners across robotics, AI, simulation, gripping technology, and industrial infrastructure.

A big thank you to all partners involved in bringing this demonstrator to life at Hannover Messe: Ensenso, Gessmann, KUKA, NVIDIA Omniverse, Schmalz, Schunk, Vathos, Yaskawa, Zimmer Group

Wandelbots and Vathos Announce Technology Partnership – AI-Based Vision Expands the NOVA Ecosystem

Lukas Krettek — Sun, 29 Mar 2026 22:00:00 GMT

Wandelbots and Vathos announce their strategic technology partnership. The goal of this collaboration is to simplify access to AI-based robot vision and set new standards for scalable automation.

With the integration of Vathos into Wandelbots NOVA, we are strategically expanding our ecosystem with a key building block for Physical AI: intelligent, vision-guided robotics.

AI-Based Perception for Dynamic Production Environments

Increasing product variability, changing components, and dynamic processes are pushing traditional automation approaches to their limits.

By combining Wandelbots NOVA with Vathos’ AI-based 3D object and pose detection, even complex and hard-to-predict processes can be reliably automated – from flexible pick & place to advanced assembly applications.

Platform and Marketplace Approach as a Scaling Lever

The integration of Vathos follows a fully platform-based approach:

Direct access to new functionalities without individual integration projects
Standardized deployment via a centralized platform
Reusable applications across multiple sites

This reduces complexity, shortens project timelines, and enables faster scaling of automation solutions.

From Simulation Directly to Production

Wandelbots NOVA seamlessly connects virtual planning with real-world execution.

Applications can be fully developed, tested, and optimized in a digital environment – including path planning, collision detection, and process logic. In combination with Vathos’ perception capabilities, even vision-guided applications can be reliably validated upfront.

Validated applications can then be transferred directly to the shopfloor with minimal adjustment effort.

This results in:

Shorter commissioning times
More stable ramp-ups
Reduced operational risks

Economic Impact Across the Entire Lifecycle

The partnership addresses key economic drivers in automation:

Faster implementation reduces time-to-production
Lower engineering effort through standardization and reuse
Higher equipment availability through robust, adaptive processes
Predictable rollouts enabled by virtual validation
Long-term flexibility through a hardware-agnostic approach

Overall, this improves both the efficiency of individual projects and scalability across multiple sites.

Wandelbots NOVA as a Platform for Physical AI

Wandelbots NOVA is the central platform for developing and operating robotic applications and forms the foundation for Physical AI on the shopfloor.

With partner solutions like Vathos, Wandelbots is building an open ecosystem where specialized technologies integrate seamlessly. The Marketplace plays a key role by making new functionalities rapidly available and scalable.

Hardware Independence as a Strategic Advantage

Wandelbots and Vathos consistently follow a hardware-agnostic approach.

Companies can freely choose and replace robots and 3D cameras without having to redevelop their applications. This reduces dependencies and increases long-term investment security.

Outlook

The partnership with Vathos marks another step in expanding the Wandelbots NOVA ecosystem, adding AI-based perception for vision-guided robotics to the platform.

As a central platform for software-defined automation, Wandelbots NOVA enables the rapid implementation, flexible extension, and cross-site scaling of robotic applications—together with partners like Vathos, laying the foundation for Physical AI on the shopfloor.

Bringing Embodied AI to Life: Simplifying Robotics Development with Wandelbots NOVA and NVIDIA Isaac Sim

Wandelbots — Sun, 15 Mar 2026 23:00:00 GMT

Robotics is entering a new era.
Advances in AI, simulation, and compute are transforming how robots are developed and deployed. Instead of rigid automation systems that require complex programming and specialized expertise, robots are becoming intelligent systems that can learn, adapt, and interact with the physical world.

This shift is driven by emerging paradigms like physical AI and embodied AI, where machines are capable of reasoning about real-world interactions and acting within dynamic environments.

But for developers and robotics teams, one challenge remains:

How do you actually build and deploy these intelligent robotic systems efficiently?

At Wandelbots, we believe the answer lies in combining powerful simulation environments like NVIDIA Isaac Sim with intuitive robotics tools like Wandelbots NOVA.

Bridging Simulation and the Real World

Simulation has become a cornerstone of modern robotics development. With NVIDIA Isaac Sim, developers can create physically accurate digital environments to design, test, and validate robotic applications before deploying them on real hardware.

By integrating Wandelbots NOVA with Isaac Sim, developers can move seamlessly between simulation and real-world execution, accelerating development cycles and reducing the complexity of deploying robotic applications.

Making Robotics Development Accessible

Robotics development has traditionally been complex and fragmented, often tied to proprietary robot programming environments.

Wandelbots NOVA changes this by providing a hardware-agnostic robotics platform that allows developers to build automation applications across different robot brands through a unified software layer.

Instead of focusing on robot controllers, developers can focus on building robotic applications and intelligent behaviors.

Teaching Robots in Simulation with Ghost Teaching

One of the most intuitive ways to program robots inside Isaac Sim is “Ghost Teaching”.

Instead of writing complex motion code, developers interact with a virtual “ghost” representation of the robot’s end effector inside the simulation.

By moving this ghost object through the environment, they can visually define robot motion paths and interaction points.

This makes programming robots inside simulation feel much more natural and interactive.

The workflow is simple:

1. Build your robotics environment inside NVIDIA Isaac Sim

2. Connect the scene with Wandelbots NOVA

3. Move ghost objects to define robot motions and paths

As the ghost object moves, the robot follows the defined poses, allowing developers to quickly create motion sequences directly within the simulated environment.

These motions can then be transferred directly into robot programs, enabling rapid iteration and testing without touching the real robot until the application is ready.

For developers, this dramatically simplifies one of the most time-consuming parts of robotics development: defining and validating robot motion.

Supporting Developers Building Physical and Embodied AI

Robotics is increasingly becoming an AI development challenge.
Developers are no longer only writing motion scripts, they are building systems that combine perception, learning, simulation, and real-world control.

This is where the concepts of physical AI and embodied AI become important.
Physical AI enables machines to understand physical interactions such as forces, materials, and motion dynamics. Embodied AI takes this further by embedding intelligence directly into robotic systems that must perceive and act in real time within the physical world.

To build these systems, developers need environments where they can:

simulate robotic interactions
train AI models safely
test robot behavior at scale
deploy learned behaviors to real machines

The combination of Isaac Sim, Isaac Lab, and Wandelbots NOVA enables exactly this workflow.

Simulation environments provide the foundation for training and validating AI models, while Wandelbots NOVA provides the platform that connects those models to real robotic systems.

From AI Research to Real-World Automation

The convergence of simulation, AI, and robotics platforms is enabling a new generation of intelligent automation.

Instead of static robot programs, developers can create systems that learn from data, adapt to new situations, and continuously improve.

This is especially important in modern manufacturing environments characterized by:

high product variability
frequent process changes
increasing labor shortages

AI-driven automation systems can handle these challenges by enabling robots to adapt their behavior dynamically, reducing the need for constant reprogramming.

By integrating AI development environments with robotics platforms, Wandelbots NOVA helps bridge the gap between AI experimentation and industrial deployment.

Empowering the Fifth Industrial Revolution: How Wandelbots and Microsoft Are Enabling Physical AI in Manufacturing

Marwin Kunz — Wed, 19 Nov 2025 23:00:00 GMT

A new era for industrial innovation

Manufacturing is entering a new age. Across industries – from automotive to electronics – companies are realizing that the future of competitiveness depends on their ability to connect physical operations with intelligent, adaptive systems.

We call this transformation the Fifth Industrial Revolution: a world where artificial intelligence extends beyond data centers and software into the physical realm – the rise of Physical AI.

In this new era, robots are no longer rigid machines executing pre-programmed tasks. They are learning, self-optimizing systems that collaborate with humans, adapt to change, and continuously improve. But getting there requires a fundamental shift in how we think about automation.

What if the biggest unlock isn’t another robot?

What if the biggest unlock for your factory wasn’t an additional robot or another MES upgrade, but the ability to run automation with the same reliability, governance, and scalability your enterprise already expects from the cloud?

Manufacturing leaders are confronting a new reality:business cycles are accelerating, AI demands integrated data, engineering capacity is limited, and production systems must adapt faster than traditional automation architectures allow.

Cloud providers such as Microsoft have built the digital backbone – identity, governance, high-performance compute, data services – required to run industrial workflows at scale. NVIDIA provides advanced simulation and accelerated computing for modeling physical behavior.

The question is no longer whether these capabilities exist, but how manufacturers can combine them to modernize the operational core of their factories.

The limiting factor is rarely strategy. It is the disconnect between the shop floor and the enterprise systems above it.

The collaboration that makes it possible

At Wandelbots, we believe software is the key to unlocking this revolution. Our platform, Wandelbots NOVA, acts as the software layer for robotics, enabling robots to learn, communicate, and improve – regardless of brand or model.

In most factories today, automation logic is still embedded in isolated controllers across robots, stations, and tools. This creates a structural gap: MES and ERP systems hold the business logic, Azure holds compute and intelligence, but the shop floor cannot provide the contextual, real-time data needed to close the loop.

A software-defined execution layer – such as Wandelbots NOVA OS – bridges this divide by providing a unified interface for heterogeneous equipment. Once operational data becomes standardized and accessible, higher-level systems can finally integrate production behavior, quality signals, and asset context into enterprise decision-making.

Together with Microsoft, we are building the foundation that makes this transformation possible. NOVA connects the Operational Technology (OT) world of robotics with the Information Technology (IT) world of cloud and AI.

Built on Microsoft Azure, NOVA Cloud provides a secure, scalable infrastructure to manage robots at enterprise scale. Using services such as Azure Kubernetes Service (AKS), Azure Arc, and Azure Fabric, NOVA enables seamless cloud-to-edge orchestration, so robots on the factory floor can be programmed, monitored, and optimized directly from the cloud.

From connected data to intelligent operations

Since early 2025, we’ve strengthened NOVA’s data layer through close collaboration with Microsoft and NVIDIA.With NOVA Cloud, manufacturers can now define which data to extract, analyze, and iterate on – empowering fleet management, advanced path planning, continuous optimization, and predictive operations.

The result is a bi-directional data flow between the shopfloor and IT systems, integrating seamlessly with management tools, BI systems, or third-party services.

By closing this loop, companies can:

establish global standards to simplify operations and accelerate rollouts,
drive continuous process optimization, for example achieving near-zero quality issues,
and build the foundation for self-optimizing, dark-factory concepts.

In other words, automation stops being a black box and becomes a transparent, software-defined capability that the entire enterprise can reason about.

Using simulation as the engine for continuous improvement

Once the operational layer is connected, simulation becomes a strategic asset rather than a side project.

NVIDIA Omniverse and Isaac Sim, running on Azure, enable teams to test, optimize, and validate new workflows before deployment. Simulation-first engineering reduces downtime, accelerates changeovers, and creates the rich data foundation required for AI-driven optimization.

This creates an end-to-end flow: shop floor → unified execution layer → simulation → cloud intelligence → governed deployment → shop floor

It’s a continuous improvement loop that makes Physical AI practical – grounded in real data, real constraints, and real production outcomes.

Driving progress for people, not just machines

The Fifth Industrial Revolution is not about replacing people – it’s about empowering them.

Wandelbots’ mission to democratize robotics remains unchanged: we enable anyone – regardless of technical background – to teach, deploy, and manage robots easily. A software-defined layer means operators, engineers, and developers can collaborate on automation using tools and interfaces that make sense for their roles.

Our collaboration with Microsoft amplifies this vision. Together, we’re bringing the power of cloud, data, and AI into the hands of every manufacturer – so they can adapt faster, operate smarter, and shape the next generation of intelligent, sustainable production.

Looking ahead

As the lines between digital and physical continue to blur, Wandelbots NOVA will evolve to support the next wave of robotics – from industrial arms to humanoids and drones.

With the integration of Generative AI and foundation models, NOVA will ensure that even the most advanced robots remain accessible, usable, and part of a continuously learning ecosystem. New robot forms will require new programming concepts, and our platform is designed to make those concepts available to real factories, not just research labs.

The Fifth Industrial Revolution has begun.And together with Microsoft, we’re making Physical AI the driving force behind it.

At Microsoft Ignite, Wandelbots is presenting alongside Microsoft and NVIDIA to showcase how a software-defined automation strategy enables Physical AI in real factory environments. The sessions highlight how Wandelbots NOVA connects robotics with Azure services and NVIDIA simulation to deliver enterprise-ready automation that is adaptive, data-driven, and scalable across global operations.

Your Guide to Visiting Wandelbots in Dresden

Carrie Eyerly — Mon, 29 Sep 2025 22:00:00 GMT

Coming to the Wandelbots headquarters? Here's practical information to help plan your visit to Dresden, Germany.

Wandelbots Office Address

Tharandter Str. 33

01159 Dresden

Germany

Where to Stay

All recommended hotels are in Dresden city center, less than 10 minutes by taxi to Wandelbots.

Steigenberger Hotel de Saxe - Premium stay, located at Neumarkt directly across from Frauenkirche.
HYPERION Hotel Dresden am Schloss – Elegant choice next to the Royal Palace.
Motel One Dresden am Zwinger – Modern and budget-friendly, near Zwinger Palace.

Getting Around Dresden

Reaching the Wandelbots office is easy.

A taxi ride from the city center takes less than 10 minutes.

Taxi: Call the central hotline at +49 351 211 211 or use the Taxi Deutschland App. Popular apps like Uber or FreeNow are not available in Dresden.

Limousine Service: Pre-book with Stilvolle Mobilität – Chauffeur Service 8x8 for a comfortable transfer.

Airports and Travel Connections

Most international travelers connect via Frankfurt (FRA) or Munich (MUC) to Dresden Airport (DRS). The Dresden airport is just 20 minutes by taxi to the city center or Wandelbots office.

Alternative routes:

Fly into Berlin (BER) or Prague (PRG) and continue to Dresden by limousine transfer (approx. 1.5 hours) or high-speed train with Deutsche Bahn (approx. 2 hours).

Download the PDF

From Rigid Automation to Physical AI: The 4 Levels of AI-Powered Robotics You Need to Know

Stephan Hotz — Mon, 11 Aug 2025 22:00:00 GMT

A Pragmatic Guide to AI in Industrial Automation

Artificial Intelligence (AI) is transforming robotics and industrial automation pushing the industry from rigid, rule-based systems toward flexible, context-aware autonomy.

However, with AI evolving at breakneck speed, it’s easy to get lost in hype.

This position paper introduces a pragmatic 4-level framework that helps assess the current state of AI in robotics. Each level is examined from two key angles:

Robot View: what robots can actually do at that level

Developer View: how developers build and interact with robots

The vast majority of deployed automation today still operates at Level 0 or Level 1.

Level 2, where AI augments development, is gaining traction. Levels 3 and 4, involving learned behaviors and agentic autonomy, are beginning to emerge but require a fundamentally new kind of platform.

Wandelbots NOVA is built as that platform. Wandelbots NOVA Operating System (OS) and NOVA Cloud, provide the infrastructure to unify development, enable large-scale data collection, and deploy modern AI workflows across real robot hardware bridging today’s industrial reality with tomorrow’s intelligent automation.

Industrial Automation Is Entering Its AI Era

AI is fundamentally transforming the landscape of robotics and industrial automation.

The speed, flexibility, and intelligence that AI systems bring are redefining what machines can do. They are unlocking levels of productivity, adaptability, and operational scale that were previously unimaginable. AI is no longer a futuristic vision; it's a driving force behind today's most advanced manufacturing and robotic systems.

Yet with opportunity comes complexity.

The rapid evolution of AI has introduced an incredible abundance of tools, models, approaches, and directions. For professionals and decision-makers, it's more important than ever to clearly understand the current state of AI within the robotics and automation industries. A grounded perspective helps navigate this landscape with focus and intent, prioritizing initiatives that deliver real value.

In this environment of hype and high expectations, distinguishing between buzzwords and genuine technological breakthroughs is essential. This article offers a structured framework to help you assess where AI is making tangible progress in robotics, where the greatest potential lies, and where to focus for practical impact.

Analyzing the Status Quo

To navigate the rapidly evolving field of robotics and AI, this framework organizes the journey into levels that clarify both current capabilities and future potential. These levels offer clarity from two perspectives: the robot solution view, what tasks robots can reliably perform and the developer view, how engineers and programmers interact with robotic systems. By distinguishing between levels of maturity, you can better assess both the promise and the current limits of AI in automation.

Level 0: Heuristics & Optimizers (Not AI)

Definition: Automation Intelligence based on hardcoded rules, parameter optimizations, or simple control loops with no learning involved.

Robot View: Executes fixed tasks and programs with high precision, but no flexibility. Typical examples include welding, gluing, or palletizing routines in tightly controlled environments.

Developer View: Programs the robot via proprietary languages or logic blocks (e.g., RAPID, KRL, PLCs). All behavior must be explicitly defined and tuned.

Example: A KUKA robot spot-welds a car frame on a fixed jig.

Wandelbots NOVA Context: NOVA enables modern programming approaches even for traditional rule-based tasks by abstracting vendor-specific code into Python and providing a cell operating system for deployment and convenience features. The intelligence level remains fixed unless combined with higher-level tools. Bringing current robot cells onto a unified platform and into a single language, while opening proprietary controller systems for data collection, forms a crucial building block for Physical AI.

Level 1: AI-Powered Perception (Task specific, highly specialized AI)

Definition: Task-specific AI models, mostly in vision, enabling robots to recognize objects or features. No reasoning or learning beyond initial training.

Robot View: Gains perception abilities (e.g., see parts, detect defects) to improve flexibility in semi-structured environments.

Developer View: Integrates pre-trained vision models or tools (e.g. Cognex, Zivid) into robot workflows for pick-and-place, inspection or sanding jobs.

Example: A Yaskawa robot uses a camera to scan a workpiece, create a 3D point cloud out of a scan, and make use of an algorithmic path planner to define and execute a robot path.

Wandelbots NOVA Context: Vision-based capabilities are and can be integrated with NOVA’s Python SDK, where object detection models trigger specific robot tasks based on coordinates or advanced path planners.

Level 2: AI-Augmented Programming (AI supporting the Developer)

Definition: AI supports the development process via code suggestions, natural language prompts, vibe coding or adaptive trajectory generation. This becomes even more powerful when the robot setup is equipped with vision systems and sensors, and programming can be further abstracted away.

Robot View: Still relies on programming but with greater flexibility in execution (e.g., path planning, interpolation).

Developer View: Uses tools like GitHub Copilot, LLMs, or custom SDKs (e.g., NOVA Python SDK) to accelerate development by code generation or even simply relying on vibe engineering (natural language prompts to generate executable skills).

Example: A developer fine-tunes a pick-and-place application using AI-generated Python code in Microsoft Visual Studio Code (VS Code), with real-time simulation in NVIDIA Omniverse or Rerun.

Wandelbots NOVA Context: Developers use NOVA Developer Tools, specifically the VS Code extension to bring AI-generated code into their workflow. GitHub Copilot-style AI support helps write and modify NOVA programs, reducing ramp-up time for non-experts. As robot programming in Wandelbots NOVA is based on Python and the convenience SDK that is included as a standard package for developers it ready made for being connected to state-of-the-art AI models developed in Python.

Level 3.1: Learned Motion Intelligence (AI-driven systems that adapt quickly)

Definition: Robots execute trajectories produced by a learned policy that is trained via large-scale vision-language-action (VLA) imitation or reinforcement learning (RL). This policy masters low-level motion in dynamic scenes and generalizes across part and pose variants.

Robot View: Executes complex trajectories that emerge from unsupervised (or weakly supervised) training - often first in simulation - rather than being programmed manually.

Developer View: Instead of writing motion code, engineers curate demonstrations, simulation scenarios, and reward tweaks. Training runs on simulators such as NVIDIA Omniverse/Isaac Sim. The policy is then deployed through a runtime, such as Wandelbots NOVA.

Example: A robot arm that was pre-trained as a VLA model on millions of vision-language-action pairs, and then lightly fine-tuned in simulation to pick up a flexible gasket. The final policy can be transferred to the real cell with minimal adjustments.

Wandelbots NOVA Context: NOVA's low-level motion abstraction and Omniverse connectivity allow developers to fine-tune or validate VLA/RL policies on different types of hardware before putting them into production. Additionally, NOVA Cloud acts as the data collectors for all robot cells and systems running on NOVA OS therefore providing the data backbone for making reinforcement learning possible across shopfloors.

Level 3.2: Context-Aware Agentic AI (Learned Planning Intelligence)

Definition: Systems that understand the environment and intent, decompose a high-level command into a sequence of skills, and autonomously execute those skills, providing full planning intelligence.

Robot View: A single language command ("bolt this bracket") triggers perception, task decomposition, and dynamic chaining of skills, which is indispensable for highly dynamic production settings and general-purpose or humanoid robots.

Developer View: The focus shifts toward integrating a modular library of action and perception skills, system integration, and providing multimodal data (e.g., 2D/3D vision, sensor data and technical asset inputs), as well as effective prompting.

Example: A humanoid robot interprets the command "bolt this bracket" via its VLA planner and then selects verified skills: detect bracket → grasp → align → bolt-tighten.

Wandelbots NOVA Context: NOVA is positioned to serve as the execution layer in a broader agentic stack. Its API enables the seamless execution of skills within multi-step processes provided by a planning model running in the cloud or on-premises infrastructure.

Level 4: Super-intelligent AI (the longer-term future)

Definition: Theoretical AI surpasses human intelligence across all dimensions, including creativity, strategy, and physical coordination.

Robotics Context: An independent management-level AI managing robot fleets, infrastructure and even governance across an entire factory.

Example: Popularized in science fiction e.g., "Her," HAL 9000, or "Ex Machina", but not a reality today... yet.

Putting Level 3 into practice with Wandelbots NOVA

Overview

To assess NOVA’s capabilities as a robust data collection and execution layer for modern robot learning workflows, a closed-loop setup was created within Omniverse Isaac Sim. In this environment, the well-known Push-T task was recreated using a UR robot model from the NOVA asset pack.

The objective: guide the robot to push a T-shaped object across a table into a designated target area. Using NOVA’s Omniservice, the virtual scene was orchestrated, simulated cameras were connected, and NOVA's API was leveraged to control the robot and collect proprioceptive data (joint positions, end-effector pose) during human demonstration. Data was collected over several dozen episodes using simple keyboard inputs to control the robot while monitoring the real-time simulation in Isaac Sim. A Diffusion Policy was then trained from scratch, empowering the robot to successfully push the object to its goal based solely on camera inputs and the robot's proprioceptive data.

Key Take-aways

NOVA was invaluable as the "glue" between the simulation environment and robot control. Its asset library sped up cell setup, the Omniverse Connector handled environment randomization and data capture, and NOVA OS translated policy outputs into robot motion. LeRobot’s dataset and training utilities were used, as well as the tools for data aggregation and model training. Despite the LeRobot framework's convenience and encouraging community backing, its lack of support for industrial robots highlighted NOVA’s unique strength as the execution backbone for real-world use cases.

Implications

Synchronizing camera feeds, proprioceptive signals, and sensor data quickly enough when the GPUs hosting the policy and the controllers are on different machines highlighted a clear pain point. Looking ahead, streamlined cloud pipelines for automated data collection, preprocessing, and monitoring will be essential as multimodal sensing becomes more prevalent on the shop floor. While NOVA simplifies setup, runtime management, and robot control in the simulation gym, future work will focus on fully automating data pipelines and providing turnkey inference tools that connect pretrained models directly to industrial robots in simulation and production environments. This will allow for fine-grained monitoring of performance in one place. Thus, NOVA is already well placed today to help developers step into Level 3 with their robotics and automation projects. It also is uniquely placed to help developers and companies overcome the commonly recognized lack of data problem that stands in the way of a broader adaptation of physical AI application in actual industrial setups.

Conclusion

Most productive robot automation in industry today still operates at Level 0 and Level 1. These systems are mature, reliable, and deeply integrated into manufacturing processes, but they are fundamentally constrained by rigid logic and highly specialized AI.

Momentum is building around Level 2, where AI-augmented tools are beginning to enhance how developers create robot applications. Programming is becoming more accessible, workflows more flexible, and integrations with AI tools more common. However, to fully unlock the next generation of automation (Level 2 and Level 3) a new type of platform is needed.

Traditional robot software stacks were not designed to support modern AI workflows. Moving beyond today’s limitations requires:

A common abstraction layer to unify programming across robot brands and interfaces
Open APIs and data access for capturing and leveraging the full context of robot execution
Support for feedback, data collection, and iteration loops
Seamless integration of simulation, training, and deployment environments
Infrastructure for deployment and skill execution at runtime

Wandelbots NOVA is built for exactly this shift. It serves as the operating system and platform layer that connects AI-powered development with real-world industrial deployment. By enabling Python-based programming, structured data collection, and runtime policy execution across heterogeneous robots, NOVA is paving the way for developers and companies to build and deploy intelligent robotics today.

As AI continues to evolve, Wandelbots NOVA provides the foundation to move from automation to true autonomy.

Wandelbots at NVIDIA GTC Paris part of Viva Tech, Europe’s Biggest Startup and Tech Event

Marwin Kunz — Sun, 27 Jul 2025 22:00:00 GMT

11-12 June 2025 Paris, France

NVIDIA GTC Paris brought together the companies and technologies that are driving the next era of industrial automation and making it real today.

The event showcased valuable technical exchange around software-defined manufacturing, efficient robot deployment, and scalable systems.

At the center was a shared commitment to usability, made possible with intuitive robot operating systems and control platforms, from Wandelbots, designed to empower teams and enable intelligent industrial automation.

Technical Sessions

How Physical AI Is Supercharging Robotics Automation at Volkswagen and in the Industrial Ecosystem

Christian Piechnick, CEO & Cofounder, Wandelbots
Paul Weiß, Mechanical Engineer, Wandelbots
Pavel Zakharov, Senior Director of AI Labs, Grid Dynamics
Lars Beier, Staff Software Engineer, Wandelbots

Discover how Wandelbots, an NVIDIA Inception member, is transforming industrial manufacturing and automation by addressing key challenges with a simulation-first approach. Wandelbots will demonstrate how they solved some of Volkswagen’s key manufacturing challenges, bringing significant cost and time savings to production lines. This session will cover the Wandelbots NOVA platform—a hardware-agnostic operating system that enables developers, system integrators, and automation engineers to build intuitive human-machine interfaces (HMIs) for physics-based digital robot training. NOVA seamlessly integrates with NVIDIA Isaac Sim™, powered by NVIDIA Omniverse™, delivering high-fidelity, physics-accurate simulations through Universal Scene Description (OpenUSD) workflows. Join us to discover how Wandelbots is making intelligent, flexible automation accessible to all.

Watch the Video

How Physical AI Is Shaping the Next Generation of Industrial Robots

Madison Huang, Senior Director of Product Marketing, NVIDIA Omniverse and Robotics, NVIDIA
Christian Piechnick, CEO & Cofounder, Wandelbots
Yan Rudall, Director, Advanced Robotic Solutions, KION Group
Anders Billesø Beck, VP, Technology, Universal Robots
David Reger, Founder & CEO, NEURA Robotics
Eduardo De Robbio, Global Strategy Lead Consumer Industries, ABB

Physical AI is transforming how industries approach autonomy and adaptability. By combining accelerated computing, large-scale simulation, and real-time edge deployment, robots are becoming more perceptive, responsive, and efficient. Join leaders from ABB, KION, NEURA Robotics, Wandelbots, and Universal Robots as they discuss how physical AI is making robots more intuitive and effective across industries—and its potential to drive the next frontier of industrial transformation.

Watch the Video

Collaboration

From Simulation to Scale: Wandelbots, Schaeffler, and NVIDIA Redefine Industrial Robotics

At GTC Paris, Wandelbots unveiled the next chapter in its collaboration with Schaeffler and NVIDIA showcasing how simulation-driven development can be deployed at scale using real-time digital twins and robot-agnostic execution.

With Wandelbots NOVA integrated into NVIDIA Omniverse, Schaeffler engineers can now design and validate complex robotic workflows virtually, then deploy them to physical robots without reprogramming. What was once considered non-automatable is now running autonomously, in production-ready form. This breakthrough demonstrates a new model for intelligent automation: Plan virtually. Execute physically. Improve continuously.

Explore how this collaboration is defining the future of scalable, software-defined manufacturing.

Read the blog post

More information about the collaboration in the Press Release by NVIDIA (11 June 2025). NVIDIA Builds World’s First Industrial AI Cloud to Advance European Manufacturing. Read the press release.

Wandelbots, EY, and EDAG at GTC Paris: Accelerating Industrial Transformation

Wandelbots joined EY and EDAG Group to demonstrate how strategic collaboration is reshaping industrial automation. Together, the partners presented a shared vision for the future of manufacturing powered by intelligent systems, rapid deployment, and a human-centered approach. This collaboration combines software-driven automation, strategic consulting, and engineering execution to help manufacturers scale faster, operate smarter, and keep people at the center from concept to deployment.

Read the full story on how Wandelbots, EY, and EDAG are enabling real transformation for the Industrial Metaverse.

Read the blog post

More information about the collaboration in the blog post by NVIDIA (11 June 2025) European Robot Makers Adopt NVIDIA Isaac, Omniverse and Halos to Develop Safe, Physical AI-Driven Robot Fleets. Agile Robots, Humanoid, Neura Robotics, Universal Robots, Vorwerk and Wandelbots launch NVIDIA-accelerated robotic systems and platforms, as NVIDIA releases full-stack safety platform for robotic development. Read the blog post.

The Physical AI Revolution in Robotics

Christian Piechnick — Mon, 23 Jun 2025 22:00:00 GMT

SoftServe and Wandelbots are changing how manufacturers use NVIDIA simulation technologies to embed flexibility and intelligence into robotic automation

Advances in automation have long driven manufacturing efficiency; the leap into Industry 4.0 and 5.0 has proved to be no exception. Particularly given the skilled labor shortages and volatile supply chains hampering Western industrial nations, doubling down on automation will be key to remaining competitive. However, with a new global average robot density of 162 units per 10,000 employees in 2023 — twice the number from only seven years ago — the traditional approach to automation innovation will not be up to the task

Inflexible and expensive, standard robotic automation systems require significant downtime and costly manual reprogramming for every new task. This rigidity makes traditional robots impractical — particularly for small- and medium-sized companies (SMEs) lacking the resources of their multinational enterprise competitors. Without a new approach, competitive dynamics could result in downsizing or even a wave of insolvency.

We see the answer to these challenges in robotics programmed using physical AI, where artificial intelligence bridges the gap between the virtual and the physical world — empowering robots to learn, adapt, and operate intelligently in dynamic environments. Keep reading to explore the physical AI revolution within robotics programming and learn about its context, technical foundations, and promise to transform manufacturing practices.

The challenges facing western industry: reshoring and skill shortages

The calls for advancements in industrial automation have grown louder as the crises facing Western manufacturers have compounded. Supply chains remain stressed in the aftermath of the pandemic and ongoing geopolitical conflict. To wit: German car manufacturers, long considered leaders in global automotive innovation, face production delays and price hikes due to reliance on overseas suppliers and consequently saw their margins suffer.

Meanwhile, industries that outsourced major segments of their supply chains are feeling intense pressure to reshore. U.S.-based companies like Intel are investing in automated chip factories to reduce dependency on suppliers in the Far East. Yet Western industries continue to grapple with a prolonged skilled labor shortage — hindering their ability to ramp up production. Only a new approach to automation can compensate for the dearth of skilled workers and reliable supply chains.

The need for smart and versatile robots

The standard approach to industrial robots prevalent in the West — often programmed with static instructions — is ill-suited to meet new, flexible production demands. While adaptative robotics alleviated some of the most arduous pain points, setting up or changing their programming requires extensive expertise, financial investments, and time.

Testing robots in the real world is furthermore slow, risky, and expensive — every mistake can damage equipment or disrupt production. These limitations lead to soaring costs for reprogramming and testing every new task, making the technology inaccessible while further exposing vulnerabilities in complex, evolving production processes.

These limitations are largely due to the reliance on an outdated technology stack, which includes proprietary programming languages and control concepts tailored to each robot manufacturer. Additionally, the lack of standardized interfaces for data access and control, coupled with the absence of integration in modern development tools and frameworks, further exacerbates these technological hurdles.

Given these pitfalls, the new industrial landscape demands a new approach to robotics. Adaptive robots need to be capable of seamlessly switching between tasks without costly reprogramming, revolutionizing factory operations for businesses of all sizes. To address these issues, modern manufacturing must deploy a new method to commission intelligent robotics systems, capable of understanding and adapting to diverse tasks autonomously.

Physical AI: the key to a new generation of robotic automation

The bedrock for this new type of automation lies in physical AI. The advancements in this technology are more than a step forward for automation; they represent a fundamental shift in how machines integrate intelligence and adaptability. By merging AI technologies with physical robotic systems, physical AI enables robots to perceive, think, and interact with their environments dynamically.

At its core, physical AI bridges the gap between the data-centric technologies associated with artificial intelligence and the necessities entailed in leveraging physical robots in real production environments. Its benefits encompass more than mere cost reductions during programming. Rather, it introduces a paradigm of flexibility, intelligence, and democratization that fundamentally changes how businesses approach automation.

Scalability and flexibility: Physical AI enables manufacturers to deploy robotics that adjust to evolving production needs and design changes. For example, a mid-sized automotive supplier can train robots in virtual simulations to adapt to new workflows or product iterations with minimal downtime and no extensive hardware modifications.
Enhanced adaptability and decision-making: Physical AI empowers robots to handle unpredictable conditions and perform intricate tasks effectively. For instance, a robotic system handling box stacking can adjust to varying box sizes and configurations through AI-based learning, eliminating prior constraints seen in traditional systems.
Cost-effectiveness and faster deployment: Simulation-based training and testing ensure robots can validate operations virtually before being introduced to physical production lines. This reduces liability, accelerates implementation timelines, and significantly lowers costs by proving feasibility without initial investments into hardware.

From data points to the shopfloor: The physical AI leap

Physical AI was long restricted to highly specialized applications. Its expansion has been powered by advancements in computational power and simulation tools that significantly lower the threshold for entry. Moreover, specific innovations have made physical AI viable for industrial robotics. Specifically, lower thresholds to harness three core components are driving its growth in industrial applications:

Reinforcement learning: Reinforcement learning uses trial and error at a high scale and pace for skill acquisition. Robots are trained with artificial incentives to optimize performance. For example, robotic arms leveraging reinforcement learning can determine the most efficient way to grip irregularly shaped objects without periodic human interference.
Large language models (LLMs): The rise of large language models (LLMs) has introduced the use of natural language in robotics programming. Robots can process commands to generate control behavior and adapt appropriately. That removes the need for specialized robotics engineers for simple yet effective tasks.
Physically accurate simulation: Lifelike simulation tools are instrumental in the deployment of Physical AI. Platforms such as NVIDIA Omniverse and NVIDIA Isaac Sim allow manufacturers to create realistic digital twins of production environments. These simulations prepare robotic programming for different setups — prior to physical commissioning. In existing installations, AI-based path generation optimizes robots’ movements, e.g. to increase output by decreasing cycle times. Simulated ideal paths can be transferred to the physical cell.

Physical AI in action with SoftServe, Wandelbots, and NVIDIA

For manufacturers seeking to navigate the complexities of reshoring and labor shortages with physical AI, a synergy between Wandelbots, SoftServe, and NVIDIA is setting a new standard. The convergence not only simplifies the robotics programming interface but also enhances operational readiness, ensuring continual alignment between simulation and real-world outcomes — at scale across production facilities.

Wandelbots NOVA + NVIDIA Isaac Sim

Wandelbots has developed NOVA, a user-friendly low-code software platform designed for easy robotic programming and making robots intelligent, software-driven assets. When combined with NVIDIA Isaac Sim’s physically accurate simulations, NOVA allows businesses to bring robots from virtual space to the real production floor seamlessly. Whether for designing a new production line (greenfield) or upgrading an existing one (brownfield), NOVA enables a seamless transition between simulation and real-world deployment.

Deploying NOVA at scale with SoftServe

With hands-on experience in both NOVA and strong expertise in developing solutions with NVIDIA Omniverse technologies, SoftServe is uniquely positioned to integrate these platforms into robust, scalable solutions tailored to real-world industrial needs. SoftServe provides clients with the capacity to scale — delivering enterprise-grade solutions that meet the demands of global manufacturers and industrial leaders.

Case study in digital twin and reinforcement learning: Volkswagen

To illustrate the potential for a physical AI-grounded approach, consider Volkswagen's project to update the assembly process for roof liners stands.

The production process for these components is notoriously complex due to the sheer size and flexibility of the workpieces, in addition to the challenges of automating the assembly. Crucially, designing robotic grippers to manipulate the components is not cumbersome but also a significant investment in time and resources. Previously, manually designing automation solutions for such intricate tasks could take years, given the vast scope for solutions and prolonged iteration cycles needed.

Using NOVA and NVIDIA Omniverse, Volkswagen was able to leverage reinforcement learning on a digital twin for its design journey. That allowed it to:

Utilize randomization to explore multiple layouts and gripper designs.
Specify material properties to align with production requirements.
Generate multiple strategies for the assembly process, breaking it into manageable sub-procedures.
Leverage a GPU cluster to test various variants in parallel.
Optimize for collision-free operations, minimal cycle times, and stable sub-processes.
Implement adaptive processes utilizing vision and force sensors with real-time adjustments based.

Volkswagen was able to derive multiple working strategies automatically in just 17 hours — a process that traditionally took months or years. That success highlights a pivotal shift towards efficiency and innovation in automotive assembly, setting a benchmark for future automation processes.

A new age of automation — what’s possible when robots think and adapt?

Physical AI is rewriting the future of robotics programming. Its scalable and adaptable nature enables manufacturers of all sizes to reap the benefits of intelligent automation. By reducing costs, increasing efficiency, and eliminating barriers for SMEs, Physical AI presents a new chapter for global manufacturing.

Wandelbots’ integration with NVIDIA Omniverse and NVIDIA Isaac Sim ensures that simulation and physical execution stay in sync, enabling real-time testing, validation, and troubleshooting. Whether deployed via cloud or on-premises, SoftServe can help companies use Wandelbots NOVA to reduce development time, lower costs, and confidently scale AI-driven robotics across their operations.

What could your production line achieve if your robots could learn, adapt, and make decisions autonomously? Stay tuned for future articles diving into specific use cases, technical details, and more. Or visit us at automatica in Munich from 24-27 June to talk to our experts personally.

This article was co-authored with Lyubomyr Demkiv, Director, Robotics & Advanced Automation at SoftServe