The first 5000-square-metre hub, planned for Italy and scheduled to be operational by Q4 2027, will be followed by a second facility elsewhere in Europe and a third serving global markets. Together, the three facilities will reproduce real industrial environments, generate high-fidelity physical data and connect that data with large-scale digital-twin simulation.

Large language models learn from vast amounts of information available online. Humanoid robots have no comparable source of data on how to act in the physical world. The Data Factory will create it by collecting data as GENE.01 performs industrial tasks and expanding those experiences through digital-twin simulations powered by AMD compute.

Building on the companies’ strategic technology collaboration, AMD will be Generative Bionics’ AI infrastructure partner and the Data Factory network will be powered by AMD Helios. Its rack-scale architecture will provide the compute foundation to process physical and synthetic data, train Physical AI models and run thousands of digital-twin scenario variations in parallel.

The Data Factory will connect real-world robot operations, simulation and AI development in a continuous real-to-sim-to-real cycle. It will allow Generative Bionics to configure and validate GENE.01 for specific industrial environments before customer deployment, then feed data from deployed fleets into future training cycles.

Industrial humanoids must learn from the conditions they will encounter at work. This requires data that captures contact, force, motion, proximity, temperature, task constraints and human interaction in realistic operating environments. For GENE.01, the dataset will include vision and motion data as well as signals captured through its full-body multimodal skin.

At 5000 square metres, the first Data Factory hub is set to become Europe’s largest Physical AI training facility for humanoid robots. It will reproduce environments selected with industrial partners and allow fleets of GENE.01 robots to perform tasks continuously in real-world conditions. These operations will generate high-fidelity physical data for training World Action Models and validating the complete robot system, including perception, control, safety behaviour and task execution, before deployment at customer sites.

Each physical environment and robot configuration will also be recreated as a digital twin. The same tasks can then be repeated across thousands of variations in layout, objects, materials, operating conditions and failure modes that would be impractical to reproduce physically. Models trained and tested in simulation will be deployed back to the robots for validation, while data from physical operations will feed the next training cycle.

As part of the planned collaboration, Generative Bionics intends to leverage AMD high-performance AI computing infrastructure to support the development of its Physical AI Data Factory network. The infrastructure is expected to provide the scalable compute capabilities needed for AI model training, processing physical and synthetic data and running digital-twin simulations. Generative Bionics will co-locate robotics, AI and data-engineering teams with the infrastructure to manage the pipeline from raw multimodal robot data to AI-ready datasets, validated models and deployment on robot fleets. The Data Factory will therefore connect physical environments, data engineering, simulation, model training and fleet deployment across a shared multi-site infrastructure.

“We are replicating the world, physically and digitally, to train humanoid robots at scale before they ever enter it,” said Daniele Pucci, CEO of Generative Bionics. “The Physical AI Data Factory is how we do it: fleets of GENE.01 learn from real industrial work, we stress-test those skills across thousands of digital-twin variations on AMD compute, and we return validated World Action Models to the field. This is the infrastructure that enables the Action Intelligence layer of Physical AI in real customer environments. With AMD at our side since day one, on board GENE.01 and now at the heart of the Data Factory, we are building this network here in Europe, so that the data, the models and the infrastructure behind our industrial humanoids are developed and operated on the continent.”

“Physical AI represents an exciting opportunity to bring the power of AI into the real world, creating new possibilities for automation and industrial innovation,” said Keith Strier, SVP, Global AI Markets at AMD. “With AMD Helios™ powering Italy’s first Physical AI Data Factory, we are providing scalable, high-performance computing infrastructure needed to turn that potential into reality. Our collaboration with Generative Bionics marks an important step in advancing European AI and industrial sovereignty, enabling the development and deployment of next-generation intelligent robotics applications while strengthening Europe’s capacity to innovate.”

gbionics.ai

The companies are working together to develop a high-performance robotic edge runtime for Humanoid’s next-generation robots. By integrating NVIDIA’s advanced technologies into its platform, Humanoid will be able to run increasingly sophisticated AI directly onboard its robots, enabling them to perceive, reason and respond to the physical world in real time. These capabilities will be critical as humanoid robots move from controlled environments toward large-scale, real-world deployment.

As part of this work, Humanoid is integrating NVIDIA’s latest robotics and edge computing technologies, including NVIDIA Jetson Thor, NVIDIA Isaac OS, NVIDIA Holoscan Sensor Bridge and Holoscan SDK. Together, they will provide the foundation for processing large volumes of sensor data, running AI models onboard, and coordinating perception, reasoning and control in real time.

NVIDIA technologies already play an important role across Humanoid’s development stack. Since its early days, Humanoid has used NVIDIA Isaac Lab and NVIDIA Isaac Sim open frameworks to train and simulate models before deploying them on physical robots, creating a connected workflow from training and simulation through to real-world operation.

A key part of the new architecture is NVIDIA Jetson Thor onboard compute. By moving to the platform, Humanoid has been able to consolidate two onboard computers into a single system and maintain the computing capacity, with potential additional headroom in the future. At the same time, Jetson Thor’s unified CPU and GPU memory reduces unnecessary data movement and latency and simplifies how different workloads share computing resources.

Humanoid is also integrating NVIDIA Holoscan Sensor Bridge to create a faster path between the robot’s sensors and its AI models. Camera data can be placed directly into GPU-accessible memory, reducing unnecessary data copies and CPU workload and enabling lower-latency processing across perception, inference and control.

Beyond increasing computing performance, the work with NVIDIA helps Humanoid simplify the robot’s overall architecture. NVIDIA Isaac OS, an open, deterministic runtime framework, provides a GPU-native runtime for coordinating multiple robotics workloads, while Multi-Gigabit Ethernet provides up to 100Gb of bandwidth for sensor data and command and control signals. Together, these technologies can reduce hardware and software complexity, making the platform easier to develop, test and ultimately scale.

“NVIDIA has been an important technology partner for Humanoid from the early stages of our journey, and our work together continues to evolve as our robots become more capable. Building general-purpose humanoid robots requires significant computing power and an architecture that can bring sensing, AI and control together in real time. NVIDIA’s robotics platform gives us a strong foundation to do that. Together, we can unlock new levels of autonomy and bring more capable humanoid robots into the real world,” said Artem Sokolov, Founder and CEO of Humanoid.

The companies will continue working together as Humanoid advances its robotics platform and prepares its next generation of robots for increasingly complex real-world applications.

thehumanoid.ai

The company will use the funds to grow its team and scale its software solution across industries in pursuit of its goal to reduce the full humanoid robot deployment process to one hour or shorter.

According to the International Federation of Robotics, there were 5 million industrial robots operating globally in 2025. However, these robots are built to repeat fixed tasks, and are unable to adapt to the high-mix, low-volume production now common in modern manufacturing, so that work still falls to human workers.

Humanoid robots are needed to automate a wider range of complex tasks. While hardware has advanced significantly in recent years, software development has struggled to keep pace, limiting real-world deployment.

Bleu Robotics takes a different approach to close the gap between hardware and software. Its technology removes the need for those large task-specific datasets and long training cycles, allowing manufacturers to deploy robots cost-effectively and at scale.

Instead of an end-to-end model, Bleu Robotics’ abductive learning model allows the robot to learn a new task from as few as ten operator demonstrations, while keeping the explainability required for industrial safety and the 99%+ accuracy that production lines demand.

The model eliminates the need for vast amounts of training data by inferring the most likely intent behind an operator’s demonstration, including the task’s goal and constraints, and determining how to reproduce it reliably.

The software can be plugged into any humanoid platform. Operators teach it new tasks directly at the workstation using Bleu’s training devices, handheld grippers that remove the need for coding, and can retrain the same robot as production needs change or move it to another site.

“Humanoid robots in manufacturing are the next automotive sector, with the potential to help Europe and the US reshore production. But most robotics companies are trying to train humanoids with millions of data points and build fully general-purpose models, says Benoît Berkoukchi, co-founder of Bleu Robotics.

“That approach isn’t suited to manufacturing. There isn’t enough data on factory work, and there won’t be internet-scale data, the kind LLMs were built on, for robots for a long time. Our methodology means we can do more with less.”

Bleu Robotics was founded in Paris in December 2025 by Benoît Berkoukchi, Jean-Baptiste Mouret and Serena Ivaldi. Berkoukchi spent years managing large teams on automotive assembly lines at Renault. Mouret and Ivaldi are research directors at INRIA, having spent the last 15 years as leading academics in the AI and robotics space. They have published hundreds of scientific papers in prestigious journals, including in Nature, and organized some of the world’s leading humanoid robotics conferences.

The company is reported to be working with two major industrial companies while building partnerships with robot distributors. With each deployment, the model will benefit from real-world data that makes it more reliable and faster to roll out. The ambition is to become the software of choice for manufacturers to deploy and manage robotic work at scale.

bleu-robotics.com

Applied effectively, AI in manufacturing can help engineers, planners and operators evaluate more information, explore alternatives faster, reuse expertise and automate routine work. The opportunity extends beyond productivity. It can improve decision speed, increase responsiveness to change and help organizations make better use of the knowledge they have accumulated over years of industrial experience. 

As industrial operations become more complex, this ability becomes increasingly valuable. Products are more configurable, production networks more distributed and supply chains more dynamic. Critical knowledge is often spread across experienced employees, enterprise systems, documents, spreadsheets and plant-specific processes.

This is where agentic AI in manufacturing becomes particularly important. Agentic AI can understand intent, reason across context and coordinate multi-step workflows across applications and processes. Rather than simply returning information, an agent can help determine what needs to happen next and initiate or coordinate actions within a governed workflow. 

Generative AI is an important related technology. It is particularly suited to creating and adapting content, processes and proposed outputs. The two can work together. An AI agent may call on generative AI when something needs to be created, just as it may draw on simulation, optimization or predictive intelligence when a task requires those capabilities. The distinction is important: generative AI primarily creates, while agentic AI coordinates capabilities around an objective.

The opportunity is therefore not simply to give people more data or more AI-generated content. It is to bring different forms of intelligence together so people can understand complexity, explore more possibilities and act on trusted information with greater confidence.

Why does general generative AI fail in critical manufacturing environments?

By general-purpose generative AI, we mean broadly trained tools such as ChatGPT or Microsoft Copilot that can answer questions, summarize information and generate content across many topics. They have demonstrated significant value for everyday productivity, but moving from experimentation to dependable operational use has proved more difficult. Gartner reported that at least half of GenAI projects had been abandoned after proof of concept by the end of 2025, citing poor data quality, inadequate risk controls, escalating costs and unclear business value. That helps explain why general-purpose models alone are not enough for industrial environments, where AI must operate within specific products, processes, constraints and operating conditions.

In manufacturing, an answer that merely sounds plausible is not enough. Decisions may affect product quality, worker safety, compliance, machine utilization, material availability, production schedules, cost and customer commitments.

That means industrial AI must be grounded in more than text. It needs access to governed information, validated engineering knowledge, operational constraints and models that represent how industrial systems actually behave. Generative AI still plays an important role, but its outputs become far more valuable when they are based on trusted industrial context and connected to the systems in which work actually takes place.

Generative AI can create new content, proposed processes, instructions or alternatives. Agentic AI can interpret an objective, determine which capabilities are required and coordinate a workflow that may include generative AI alongside predictive models, optimisation, simulation and other technologies.

Human supervision remains essential. AI may analyse, propose, generate or coordinate actions, but people need visibility into the information being used, the assumptions being made and the potential consequences before critical decisions are approved.

How can manufacturers use agentic AI to mitigate the loss of tribal knowledge?

One of the most immediate industrial opportunities for agentic AI involves knowledge. Many manufacturing processes still depend heavily on experienced individuals who understand why certain decisions were made, which exceptions matter and how formal processes need to be interpreted in practice. That creates a growing challenge as experienced workers retire; organisations struggle to recruit specialised skills and newer employees need to become productive more quickly. AI agents can help make enterprise knowledge easier to access in the context of the work being performed.

Instead of searching through documents or trying to find the person who remembers a particular decision, an engineer might ask why a manufacturing process was configured a certain way, what standards apply or what previous decisions are relevant to the current situation.An agent can connect that request to available enterprise knowledge, operational context and historical information, helping surface relevant guidance directly within the workflow.

The important point is that AI does not replace industrial expertise. It helps organizations make that expertise easier to discover, apply and reuse. Knowledge that was once difficult to find or concentrated in a few individuals can become more accessible across people, programs and locations.

Agentic approaches can therefore help organizations move beyond using AI simply to retrieve information. They provide the potential to put accumulated knowledge to work by combining context, reasoning and actions within industrial workflows. AI-Powered Virtual Companions and Industrial Knowledge

Shifting from disconnected data to integrated industrial world models

Industrial AI becomes significantly more valuable when it understands the environment in which decisions are being made. General AI systems may begin with documents, databases or individual data sources. Industrial operations require a richer representation that includes products, production systems, supply chains, resources, processes, constraints and real-world operating conditions. This is where industrial world models become important.

Rather than treating information as isolated data points, a world model represents relationships between the different elements of an industrial system. It provides context for understanding not only what something is, but how it relates to everything around it.

Virtual models of products, production environments and supply chains can provide an important part of that foundation. Connected to operational information, they allow organisations to experiment, simulate behaviour and evaluate the potential consequences of decisions before implementing them in the real world. Agentic AI provides a practical way for people to interact with this richer industrial context. For example, a planner responding to a production disruption might begin with a simple question. Predictive intelligence could identify the likely impact, optimisation could evaluate alternative schedules and simulation could help assess the consequences. The agent can coordinate the workflow and bring the relevant information back to the user. The agent does not replace those capabilities. It helps bring them together around the user's objective.

As industrial AI develops further, this relationship between AI agents, structured industrial knowledge and richer models of the physical world could become increasingly important. Industry world models offer a way to ground AI in relationships, constraints and behavior rather than relying on disconnected information alone.

What are the key performance indicators (KPIs) and ROI of industrial AI?

The value of industrial AI ultimately comes from improving how quickly and effectively organizations can turn information into decisions and decisions into action.
At an operational level, that can mean less manual work, faster response to disruption, better use of scarce expertise, improved quality, more consistent execution and stronger planning and resource utilisation.

The benefits can be significant. Representative examples from manufacturing engineering and machining include:

• Up to 50% improvement in manufacturing efficiency
• Up to 25% reduction in industrialisation lead time
• 90% to 95% reduction in MBOM creation and update effort
• Up to 50% reduction in machine setup time.

These examples illustrate the potential at the task and process level, but the greater value comes from what those improvements enable across the business.
Faster engineering and planning cycles can help companies bring products to market sooner. More reliable decisions can improve delivery performance and customer responsiveness. Better access to expertise can reduce dependence on scarce skills.

At the corporate level, the cumulative effect is the ability to operate with greater speed, resilience and flexibility. That can strengthen competitiveness by helping companies innovate faster, respond to customers more effectively, protect margins and make better use of their existing knowledge and resources.
The value of industrial AI is therefore not simply measured in minutes saved on individual tasks. It is measured in how effectively the organization can convert knowledge into better business outcomes.

Shifting from simple AI assistance to trusted industrial transformation

Agentic AI represents an important next step in the evolution of industrial AI. Rather than simply answering questions or generating content, AI agents can help users understand intent, navigate complexity and coordinate actions across industrial workflows. Their potential comes from being able to bring together the right information, applications and specialised capabilities around a specific objective. That may include generative AI to create or adapt content, predictive AI to anticipate risks, optimisation to evaluate complex trade-offs and simulation to test potential outcomes.

In this sense, agentic AI can become the connective layer between people and an increasingly sophisticated industrial technology environment. But the value of that capability depends on trust. Agents need to operate within governed workflows, draw on reliable industrial context and provide people with appropriate visibility and control over important decisions.

The goal is not autonomous AI for its own sake. It is to make industrial knowledge and advanced capabilities easier to access, coordinate and apply. When that happens, agentic AI can help move industrial organisations beyond isolated AI assistance toward something more significant: a trusted way to turn knowledge into decisions, decisions into action and industrial intelligence into measurable business value.

Adrian Wood is director of strategy and marketing at DELMIA

www.3ds.com/products/delmia

WITH THE EU Machinery Regulation coming into force in January 2027, machine builders face an imminent deadline  to review their compliance procedures, cybersecurity measures and engineering practices. Matthew Hallas, business development manager, OEM Drives at ABB opened the agenda by setting the scene in terms of the wider challenges currently faced by UK machine builders, from rising employment and energy costs to supply chain disruption and skills shortages. Against this backdrop, the speakers outlined how standardising machine architectures and reducing engineering complexity could help manufacturers meet regulatory requirements while bringing products to market more quickly.

Regulatory changes

Pekka Alasaari, senior global product manager, drive products at ABB presented on functional safety and the Machinery Regulation, highlighting the significance of the transition from the existing Machinery Directive to the new regulation. Unlike a directive, which allows some scope for national interpretation, the regulation establishes requirements that apply directly across the EU. For UK machine builders exporting to the EU, understanding the changes is therefore essential and manufacturers must ensure their machines meet the updated requirements when placing them on the market.

As Alasaari pointed out, cybersecurity is a particular focus. Under the new regulation, manufacturers must incorporate cybersecurity threats into their machine-level risk assessments, recognising that vulnerabilities can have implications for functional safety.

Another important change concerns software traceability. Alasaari explained that machines must maintain records of safety-related changes over a five-year period, with automatic logging providing digital evidence of modifications. This will help manufacturers investigate discrepancies between the machine’s actual configuration and its documented safety status.

Documentation requirements are also evolving. “In the machine directive era, it was mandatory to have paper documentation, now a QR code next to the machine is sufficient,” said Alasaari. However, he also emphasised that the responsibility for ensuring the complete machine complies remains with the OEM, rather than the individual component suppliers. “As a component provider, we do as much as we can to help the machine builder to comply with the standards, but still, at the end, it's the OEM that is responsible for ensuring the whole machine complies with the regulation."

Reducing engineering complexity

Beyond regulatory compliance, the webinar explored how machine builders can address the commercial pressures affecting their businesses. Hallas highlighted the importance of reducing time to market, particularly for UK manufacturers producing bespoke machinery that requires repeated engineering work. He noted that a significant proportion of machine costs sits outside the physical components themselves, with engineering, software development and assembly labour all contributing to the total.

“Half the cost of a machine is not in the cost of components,” he said, pointing to the potential savings available through better design decisions and more efficient engineering processes.

Umit Kahraman, global segment manager for machinery products at ABB, explained that combining drives, motors, PLCs and control functions within a common engineering environment could help reduce the complexity associated with configuring and commissioning machines.

Traditionally, machine builders may combine products from multiple suppliers, requiring engineers to work across different software packages and manage additional integration and verification tasks. Kahraman suggested that shifting towards configuration and reuse, rather than designing every machine from scratch, could help address these challenges.

ABB’s Automation Builder platform provides a common environment for configuring and programming drives alongside PLCs. According to Kahraman, this approach can simplify engineering workflows, accelerate commissioning and make better use of scarce technical expertise.

“Engineers can configure, programme, and manage drives directly within the PLC tool, so they don't need to learn multiple software packages or manage different database versions,” he said.

Standardisation also offers the opportunity to develop repeatable machine architectures and reusable software, helping manufacturers reduce engineering hours without compromising the functionality required by individual applications.

Efficiency, connectivity and cybersecurity

The webinar also examined the role of motor and drive selection in improving machine performance and energy efficiency. Richard Gee, UK sales manager for low voltage motors, outlined ABB’s comprehensive motor portfolio, including synchronous reluctance and liquid-cooled designs intended to deliver higher power density and more compact machine configurations. The range also includes solutions for applications requiring specific safety certifications or operation in hazardous environments.

Gee emphasised the importance of matching motor technology to the application, allowing machine builders to balance efficiency, available space and performance requirements.

“Whatever your application, I would say ABB has a package solution that we can offer, discuss, and adapt to your needs,” he said.

Lifecycle management was another consideration. Hallas highlighted ABB’s Go Select configuration tool and digital documentation resources as ways to support component selection and provide access to product information and certification.

Connectivity is also becoming increasingly important as end users seek better access to machine performance and condition data. Hallas discussed OPC UA as a means of connecting drive-level information with factory-level control systems, supporting more proactive maintenance and helping reduce unplanned downtime.

With cybersecurity remaining a growing priority as the regulatory landscape develops, Pekka Alasaari returned to the spotlight to discuss security features available in ABB drives, including user account management, firmware protection and parameter locking. He also highlighted the forthcoming Cyber Resilience Act as a further development that machine builders will need to consider.

Watch on-demand

This review provides just a taste of what was covered in the webinar, which also included an informative and wide-ranging roundtable discussion, including questions submitted by the audience on the day. If you would like to find out more, you can watch the webinar on-demand for free via the following link:

The kit lets customers set up a multi-camera system and begin validation right out of the box, shortening the path from proof of concept (PoC) to deployment. Full camera source code is included, giving customers flexibility throughout development and maintenance.

As AI vision moves toward multi-camera perception in robotics and intelligent vehicles, integrating cameras, capture cards, drivers, and host platforms has become increasingly complex, often taking six months or more before validation. The starter kit’s pre-integrated hardware and software driver enable system integrators to speed up time-to-market.

GMSL2 is a high-speed video transmission technology that originated in automotive applications and offers long-distance connectivity. Unlike plug-and-play USB cameras, it requires a dedicated capture card and platform-specific drivers. The starter kit includes four 3MP fixed-focus GMSL2 camera modules, a capture card, four FAKRA cables, a power adapter, power cords, and a quick start guide. It also provides camera source code for the widely adopted NVIDIA Jetson Orin Nano Super Developer Kit, so users can bypass component sourcing, system-level integration, driver debugging, and tuning.

“The biggest challenge in a multi-camera AI vision project is often not the AI application itself, but the camera integration and system tuning that come before it,” said CK Hsu, senior manager of the camera product business department at Innodisk. “With the GMSL2 Camera Starter Kit, Innodisk takes on the complexity of early-stage integration so customers can focus on development and move into PoC sooner.”

With the camera source code in hand, engineering teams can independently adapt the software driver to their own requirements and use the kit as a working foundation for further development. Innodisk also provides comprehensive customisation services from hardware fine-tuning to application deployment, helping customers move smoothly into design-in and mass production.

The kit supports edge AI scenarios including blind spot detection and advanced driver assistance systems (ADAS) for heavy-duty vehicles, object recognition and environmental perception for humanoid robots and AMRs. Innodisk plans to expand its GMSL2 portfolio and introduce more starter kits covering different camera models and supported platforms.

www.innodisk.com

Initially, around 100 humanoid and service robots will learn specific skills and processes in 48 training cells. The large volumes of training data will help make robots safer and more reliable.

In addition to its robot training cells, the center at the LabCampus innovation hub at Munich Airport provides substantial computing power for AI model training and computer simulations. These resources generate large amounts of data to support the development of more intelligent robots. The current focus is on industrial applications and intralogistics.

Unlike generative AI systems such as ChatGPT, which can draw on vast amounts of data from the internet, embodied AI requires training data from real-world physical interactions. Such data is currently scarce. The new training center features robot training cells where robots learn tasks including folding boxes, assembling components and using tools. At the TUM RoboGym, robotic skills can be developed and tested in realistic environments. At the opening, NEURA Robotics also unveils the next generation of its 4NE1 humanoid robot, which will subsequently be available at the TUM RoboGym.

Data generated at the TUM RoboGym flows into a curated cloud data lake, where it is available for training physical AI models. As a central scientific resource, the data lake enables scalable research into generalizable robotic skills, safe human-robot interaction and AI models for robotics.

The project’s core objective is to transfer scientific results into practical applications. “Close, continuous and flexible collaboration with the robotics industry is essential for research in physical AI. It is the only way to systematically and rapidly develop research prototypes into market-ready products,” says Prof. Achim Lilienthal of TUM MIRMI, scientific coordinator of the TUM RoboGym. 

www.tum.de

Agility recently unveiled Digit 5 as its first humanoid robot designed for cooperatively safe work at scale, allowing it to work in close proximity to people. As part of its upgrades, Digit 5 will feature a new battery system that delivers 90-minutes of runtime and charges in just 9 minutes, achieving a 10:1 run-to-charge ratio, up from Digit 4’s 2:1 ratio. This rapid charge capability is designed to keep Digit working, enabling more than two shifts in a single day and helping customers get more from each robot.

“For our customers, Digit’s uptime is the business case,” said Daniel Diez, chief business officer of Agility Robotics. “Digit 5 will deliver more than 20 hours of productive work in a 24-hour day, more than two full shifts. That’s the kind of gain that materially improves the value customers get from Digit and the economics of deploying a humanoid robot fleet. Nyobolt’s ultra-fast charging technology gets us there, and it’s one of the pieces that makes Digit 5 ready for full-scale operation.”

Nyobolt’s unique high-power battery technology is built for demanding industrial conditions, combining fast charging, exceptional cycle life, and reduced system weight. By removing the power bottleneck that has historically constrained autonomous robot fleet economics, the system addresses a key constraint on robot utilisation, that of downtime. The result is a compelling potential advantage for Digit 5 customers, who can target longer operating windows, more productive shifts and more demanding task sequences. Combined with Agility’s experience deploying humanoid robots in real commercial environments, this battery performance is designed to strengthen Digit’s value proposition for warehouse, distribution and manufacturing operations.

“Nyobolt’s technology was chosen because Digit 5 will need more power from a smaller, lighter pack, in environments where reliability isn’t optional,” said Sai Shivareddy, co-founder and CEO at Nyobolt. “Agility set an exceptionally high bar for what a battery has to withstand in live warehouse and manufacturing environments, and this partnership shows what’s possible when ultra-fast charging is engineered for the toughest conditions in physical AI.”

Early access to Digit 5 equipped with Nyobolt’s battery technology is expected to begin in the first half of 2027, with increasing availability over the following six to nine months for manufacturing, warehouse and distribution operators.

nyobolt.com

Available from 75 to 630kW, the VP3000-E combines energy-efficient motor control, integrated application functions and flexible harmonic mitigation capabilities in a compact platform. Developed as the successor to Delta’s CP2000 Series for the European market, the new series is designed for pumps, fans, HVAC, water supply, wastewater treatment and data center cooling infrastructure.

Sander van Dasselaar, head of industrial automation business platform at Delta Electronics EMEA, said: “The VP3000-E represents an important step in the continued evolution of Delta’s Industrial Automation portfolio. Customers are under increasing pressure to improve energy efficiency, optimise operational performance and reduce total cost of ownership. With the VP3000-E, we provide a flexible drive platform that combines energy-efficient operation with simplified integration. It supports our strategy to strengthen Delta’s position in key growth markets such as water infrastructure, HVAC, data centers and industrial utilities, including the critical cooling and utility infrastructure that modern data centres depend on.”

The VP3000-E supports induction motors (IM), permanent magnet (PM) motors and synchronous reluctance motors (SynRM). Dedicated functions include Automatic Energy Saving (AES) mode, Fan Traction Energy Control (TEC) and multi-pump control to optimise variable-torque applications. These include HVAC and water systems as well as data center cooling infrastructure such as chilled water pumps, condenser pumps, cooling towers, air handling units and compressors.

Designed for European requirements, the VP3000-E features a slim, lightweight design, while larger Frame L and M models use a chassis concept for simplified installation and maintenance. A C3 EMC filter is integrated as standard across the range, alongside an integrated PLC, STO SIL3 and enhanced PCB protection. DC-bus connectivity enables integration with Active Front End (AFE) solutions and common DC-bus architectures. When combined with Delta’s APF3000 Active Power Filter, the solution can support compliance with IEEE 519 harmonic distortion requirements below 5%.

Built-in component monitoring and diagnostics provide information on key components including rectifiers, IGBTs, capacitors and cooling fans. An optional LCD graphic keypad with Bluetooth and USB supports parameter management, warning and fault logs, waveform monitoring and reporting, helping simplify commissioning, diagnostics and maintenance.

The initial EMEA range covers 75 to 630kW, with additional models from 11 to 55kW planned for 2027. The series also includes the VP3000-EHS high-speed model, available from 55 kW to 450 kW for turbo blower and maglev motor applications with output frequencies of over 599Hz.

www.deltaww.com

MODERN MANUFACTURERS need faster throughput, fewer errors and smoother handling of engineering changes. A connected CAD to CAM workflow built on digital continuity helps close the gap between design and manufacturing by keeping product data, intent and revisions aligned from CAD through CAM programming and NC output.

So, what Is CAD to CAM digital continuity in modern manufacturing?

CAD to CAM digital continuity enables the seamless transfer of design data from computer-aided design (CAD) to computer-aided manufacturing (CAM), without unnecessary file translation, manual rework or loss of engineering intent. In a connected environment, engineering and manufacturing teams work from the same product information, improving consistency and reducing translation errors.

This approach is particularly valuable for companies managing complex parts and multi-axis machining, frequent design changes and revision updates, multi-CAD environments, or high-volume and high-mix production. Rather than treating CAD and CAM as separate stages, digital continuity connects them in a streamlined workflow.

In traditional workflows, CAD data is often exported, imported and reinterpreted by CAM programmers, with each handover introducing potential risks such as missing or outdated revisions, duplicated setup work, inconsistent geometry or product structures, and delays in NC programming and machine setup.

A connected CAD-to-CAM process addresses these challenges by reducing manual data transfer and file conversions while preserving design intent from engineering through to machining. It also improves revision control and traceability, shortens programming and setup times, and enables closer collaboration between design and manufacturing teams.

Practical benefits 

A digital continuity strategy delivers practical benefits across the manufacturing lifecycle, including faster time to production, as CAM programmers work with richer product context and fewer interruptions; better data reuse, with design and manufacturing information used more effectively across programmes; and more consistent machining programmes through standardised workflows that reduce variation.

It also enables a faster response to engineering change orders, with updated revisions propagating more smoothly, reduces translation errors by minimising file conversions and the risk of data loss, and improves standardisation by allowing best practices to be scaled across teams and projects.

CAM programming can become a bottleneck when programmers need to reconstruct design intent from imported CAD files. A connected CAD-to-CAM workflow reduces this friction by giving teams direct access to product structure, geometry and related manufacturing context within their CAM environment.

With less time spent reconstructing information, programmers can focus on optimising toolpaths, refining setups and developing efficient machining strategies. The result is a more efficient, repeatable and scalable NC programming process.

Building a CAD-to-CAM workflow 

A smarter CAD-to-CAM workflow supports the entire process from design to production. By keeping product data connected, manufacturers can make better decisions more quickly, respond faster to late-stage design changes, reduce rework and manual corrections, maintain consistency across revisions, and scale machining operations more effectively. Ultimately, digital continuity enables manufacturing teams to work with greater confidence and fewer disruptions.

When it comes to competitive advantage CAD-to-CAM digital continuity is more than a technical improvement; it offers practical benefits that can give manufacturers a competitive advantage. By accelerating manufacturing workflows, reducing errors and rework, streamlining the management of engineering changes, and improving overall productivity, it helps businesses operate more efficiently and respond more effectively to changing production demands.

By connecting design and machining in one continuous process, manufacturers can achieve faster throughput, better quality and a more scalable operation.

George Chen is senior offer marketing and BDM at DELMIA

www.3ds.com/products/delmia