Today, this model is evolving rapidly thanks to a concept that is gaining ground in the technology ecosystem: Robot as a Service (RaaS).

Inspired by the Software as a Service (SaaS) model that revolutionized the software industry, Robot as a Service allows companies to use robots without having to purchase them. Instead of investing in equipment, businesses pay a monthly subscription or a usage-based fee.

This model opens robotics to a much broader audience and accelerates the adoption of automation in sectors such as logistics, security, manufacturing, and agriculture.

With the rise of artificial intelligence, autonomous robots, and cloud computing, RaaS could become one of the dominant economic models in robotics in the coming decades.

Robots are no longer just expensive
industrial equipment for large
enterprises.

What Is Robot as a Service?

Robot as a Service (RaaS) is a business model in which robots are provided as a service rather than sold as a product.

In this system, the client company does not purchase the robot. It pays to use it.

The provider generally handles:

• The supply of the robot
• Installation and integration
• Maintenance
• Software updates
• Performance monitoring
• Sometimes even remote operation

The client then pays according to several possible models:

• Monthly subscription
• Pay-per-use
• Pay-per-task
• Pay-per-performance

For example, in a logistics warehouse, a company may pay based on the number of orders processed by robots. In the cleaning sector, some autonomous robots are billed per square meter cleaned.

This model transforms a heavy capital investment into an operational expense, making robotics easier to adopt.

A Rapidly Expanding Market

The Robot as a Service market is experiencing strong growth. Several market studies indicate that demand for this type of solution could increase significantly in the coming years.

According to various analyses:

• The market exceeded $1.5 billion in 2023
• It could reach more than $4 billion by 2028
• Some projections suggest over $20 billion by 2035

This growth is driven by several major trends:

• Declining sensor costs
• Advances in artificial intelligence
• The rise of cloud robotics
• Labor shortages in many sectors

Companies increasingly seek to automate repetitive tasks while avoiding heavy upfront investments. The RaaS model responds precisely to this need.

Sectors Where RaaS Is Growing Rapidly

Logistics and Warehousing

The logistics sector is currently one of the main drivers of Robot as a Service.

Autonomous Mobile Robots (AMRs) are used to:

• Transport goods
• Assist operators
• Optimize logistics flows
• Speed up order fulfillment

In some warehouses, these robots can increase productivity by 30 to 50 percent.

Security

Security robots are also offered as a service.

These robots can:

• Patrol autonomously
• Monitor sensitive areas
• Detect anomalies
• Send real-time alerts

They are used in parking facilities, university campuses, and industrial sites.

Industrial Cleaning

Autonomous cleaning robots are becoming more common in large facilities such as:

• Airports
• Shopping malls
• Hospitals
• Hotels

These robots can clean large areas autonomously while collecting environmental data.

Thanks to the RaaS model, companies can access these technologies without an initial investment.

Agriculture

Agricultural robots can be used for:

• Autonomous weeding
• Crop monitoring
• Soil analysis
• Certain harvesting operations

In this sector, pay-per-use is particularly well suited, as farmers can rent robots during specific periods of the year.

Logistics and warehousing use
Autonomous Mobile Robots (AMRs)
to increase productivity by 30 to 50 percent.

Startups Driving the RaaS Model

Many startups and technology companies are now developing solutions based on Robot as a Service.

Among the best-known players:

• Formic – subscription-based industrial robotics
• Locus Robotics – warehouse logistics robots
• Knightscope – autonomous security robots
• Hirebotics – industrial welding robots
• Cobalt Robotics – building surveillance robots

These companies no longer simply sell robots; they sell automation capacity.

Why Investors Are Interested in RaaS

The Robot as a Service model strongly attracts investors because it transforms an industrial sales model into recurring revenue.

Traditionally, robotics companies sold machines as industrial equipment, which involved:

• Long sales cycles
• Irregular revenue
• High dependence on customer capital investment

With RaaS, companies can generate:

• Monthly subscriptions
• Predictable revenue
• Long-term customer relationships

This model mirrors SaaS, which profoundly transformed the software industry.

The Challenges of Robot as a Service

Despite its potential, Robot as a Service also presents several challenges.

Robot Financing

In this model, the provider must finance the robots. This requires significant capital to deploy fleets of robots at customer sites.

Maintenance

Robots must be maintained regularly to ensure proper functioning, requiring technical teams and appropriate logistics.

Profitability

To be profitable, a robot must be used intensively. A poorly utilized robot can quickly become a cost burden for the provider.

The Future: Connected Robots and Artificial Intelligence

The future of Robot as a Service is closely linked to advances in artificial intelligence and cloud robotics.

Robots are becoming increasingly connected and capable of sharing data with cloud platforms.

This enables:

• Remote fleet management
• Continuous algorithm improvement
• Over-the-air software updates
• Performance optimization

In the coming years, robots could become true intelligent platforms connected to the cloud.

Robot as a Service represents one of the most significant evolutions in modern robotics. By transforming robots into subscription-based services, this model democratizes automation and accelerates the adoption of robotics across many sectors.

With the convergence of robotics, artificial intelligence, and cloud computing, robots are gradually becoming intelligent platforms capable of delivering automated services at scale.

In the near future, many companies may no longer purchase robots but simply subscribe to robotic capabilities.

Just as SaaS revolutionized software, Robot as a Service could become the dominant economic model of future robotics.

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The global race in intelligent robotics has reached a new milestone. Google DeepMind has announced the opening of applications for its Accelerator: Robotics, an intensive three-month program designed to support European startups developing the next generation of intelligent robots.

At a time when the convergence of artificial intelligence and physical systems is redefining industry, this accelerator positions itself as one of the most strategic programs for European founders in the field.

Accelerating the Era of Embodied AI

Over the past two years, robotics has undergone a major transformation driven by the rise of generative AI applied to the physical world, often referred to as Embodied AI. Unlike purely software-based models, these systems enable robots to understand, reason, and act within real-world environments.

With this program, Google DeepMind is clearly targeting companies working on:

• Advanced industrial automation

• Mobile robotics and autonomous navigation

• Humanoid robots

• Intelligent manipulation

• Robotic logistics

• Next-generation autonomous systems

The goal is simple: to bring robotic innovations from the lab to market faster.

Embodied AI transforms robots
into systems capable of understanding
and acting in the real world.

Direct Access to Google’s AI Technologies

The program’s main advantage lies in privileged access to technologies developed by Google DeepMind, including:

• AI models and robotic reasoning tools from the Gemini family

• Cloud and compute infrastructure tailored for robotic workloads

• Technical guidance from DeepMind researchers and engineers

• Product and go-to-market mentorship to accelerate commercialization

For many startups, access to this level of infrastructure would normally require millions of euros in investment.

A European Response to Global Competition

This initiative comes amid intense technological competition between the United States, China, and Europe in advanced robotics.

While players like:

• Boston Dynamics

• Figure AI

• Agility Robotics

are accelerating the development of robots capable of operating in human environments, Europe seeks to strengthen its startup ecosystem to compete on a global scale.

The DeepMind accelerator could thus play a key role in preventing a European technological gap.

From Research to Industrialization

Historically, one of European robotics’ major challenges has been scaling innovations to industrial levels. Many breakthroughs emerge from universities or research centers but struggle to achieve rapid commercial adoption.

The program therefore places strong emphasis on:

• Product validation

• Real industrial use cases

• Commercial strategy

• Access to international markets

This focus reflects a major evolution: robotics is now entering a phase of mass industrialization, driven by AI.

Founders who master
AI and hardware will shape
the next decade.

An Opportunity for Robotics & Physical AI Founders

For European entrepreneurs, this initiative represents far more than a standard acceleration program. It offers:

• Immediate credibility with investors

• Direct access to a global ecosystem

• Significant technological acceleration

• Strategic exposure to industrial partners

In a market where funding is becoming more selective, the support of a player like Google DeepMind can serve as a true growth catalyst.

Towards a New Generation of Intelligent Robots

The announcement confirms a fundamental trend observed by Robot-Magazine.fr: robotics is entering a new phase dominated by general-purpose AI applied to physical machines.

After the era of programmed automation and collaborative robotics, the next decade will belong to robots capable of learning and adapting in real time.

European startups now have a strategic window to establish themselves in this revolution.

Applications are open now:
https://goo.gle/robotics

For founders building the next generation of autonomous robots, the message is clear: AI and robotics are now one, and the global race has begun.

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

Embodied AI: Where Intelligence Meets Physics

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

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

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

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

3D Vision: Understanding Space Rather Than Mapping It

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

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

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

• Automated logistics

• Flexible assembly

• Handling non-standard objects

• Safe human interaction

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

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

Fine Manipulation: The Most Complex Frontier

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

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

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

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

The Convergence of the Three Breakthroughs

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

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

• Perceiving an unstructured environment

• Choosing an appropriate strategy

• Executing precise actions while accounting for sensory feedback

This synergy forms the core of next-generation robotics.

Impact on the Factory of the Future

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

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

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

Still Structuring Limits

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

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

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

A Structured Global Competition

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

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

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

Toward Widespread Cognitive Robotics

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

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

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

The True Breakthroughs Are Invisible

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

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

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

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

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Operated by the National Research Agency (ANR), the program falls under the national strategy “Robotics and Intelligent Machines.” The goal is clear: strengthen French technological sovereignty, structure the ecosystem, and accelerate the transfer of fundamental research into concrete industrial applications.

Why is this robotics program strategic for France?

Robotics is now at the heart of major industrial, energy, and societal transitions. Yet, several challenges remain:

• Navigation in complex environments
• Precise manipulation of diverse objects
• Robust perception in dynamic environments
• Energy autonomy
• Smooth AI integration

The new CNRS program aims to develop hardware and software technological building blocks capable of:

• Improving locomotion and control
• Enhancing perception and adaptability
• Optimizing physical manipulation
• Reducing energy consumption
• Increasing decision-making autonomy

Underlying all this is a major goal: designing robots that are more reliable, adaptable, and efficient, in line with France 2030’s decarbonization objectives, which dedicate 50% of investments to ecological transition.

Four Structuring Projects to Unlock Scientific Barriers

The program is organized around four major projects, each targeting a strategic technological challenge.

HAMMER: Autonomous Locomotion in Complex Environments

HAMMER aims to transform navigation capabilities of ground and aerial robots.

By combining:

• Advanced mathematical models
• Optimal control
• Learning based on large datasets

researchers aim to enable robots to move reliably in open, unstable, or poorly structured environments.

Targeted applications:

• Exploration of natural environments
• Offshore maintenance
• Operations in isolated or hazardous areas
• Inspection of critical infrastructure

The challenge is twofold: technical performance and reducing human exposure to risks.

DRMI: Next-Generation Industrial Robotic Manipulation

The DRMI project tackles the core of industrial robotics: physical manipulation.

Objective: develop robots capable of interacting with their environment with greater precision, flexibility, and reliability.

Relevant areas:

• Assembly and disassembly
• Automated sorting
• Recycling
• Industrial waste dismantling

In the context of a circular economy and reindustrialization, these advances could strengthen French industrial competitiveness.

PERSEO: Multi-Robot Perception and Cooperation

PERSEO focuses on perception, localization, and mapping in evolving environments.

A key axis: cooperation between ground, aerial, and mobile robots capable of sharing and merging their data.

Potential applications:

• Monitoring natural ecosystems
• Intelligent energy management
• Disaster detection and mitigation
• Autonomous transport

Data sharing between robots opens the way to more robust and intelligent distributed systems.

MINIRO: Micrometer-Scale Miniature Robotics

With MINIRO, research moves to the micrometer scale.

Between a few tens of micrometers and a few centimeters, physical laws change. Actuation and perception systems must be rethought.

Major prospects:

• Minimally invasive medical devices
• Robotic endoscopy
• Industrial inspection in constrained environments
• High-precision micro-manipulation

Miniature robotics could become a strategic lever in biomedicine and advanced inspection technologies.

Strategic Integration of Artificial Intelligence

A fifth transversal project is in preparation: “AI-Core-Robotics.”

Its ambition: deeply integrate AI into robotic architectures.

The rise of foundation models and generative AI opens the way for robots capable of:

• Interpreting natural language instructions
• Understanding their contextual environment
• Continuous learning
• Combining sensory and motor data to execute complex tasks

This represents a paradigm shift: moving from programmed robots to robots capable of adaptive reasoning.

This project will mobilize interdisciplinary convergence between:

• Computer science
• Mechatronics
• Micro-nanotechnologies
• Signal processing
• Materials science
• Human-machine interaction

Training Talent and Structuring the Robotics Ecosystem

Beyond technological advances, the program carries a human and strategic ambition.

Four challenges structure this vision:

• Develop technological building blocks transferable to industry
• Strengthen links between national and European actors
• Train young people in hybrid robotics–AI professions
• Attract new talent to the sector

In a context of increased competition with the United States and Asia, France must consolidate its scientific and industrial attractiveness.

France 2030: An Unprecedented Investment Framework

The France 2030 plan represents €54 billion in investments to sustainably transform strategic sectors:

• Health
• Energy
• Automotive
• Aerospace
• Space
• Industry

Robotics occupies a central place, at the intersection of:

• Ecological transition
• Industrial sovereignty
• Intelligent automation
• Reindustrialization

Program management is overseen by the General Secretariat for Investment, with support from ANR, ADEME, Bpifrance, and Banque des Territoires.

What This Means for the French Robotics Industry

With this program, CNRS is not just funding academic projects. It:

• Structures an ecosystem
• Aligns research and industry
• Prepares the next generation of intelligent robotic systems
• Strengthens France’s position in global competition

In a context marked by the rise of AI, industrial robotics growth, and international competition, France demonstrates a clear ambition: not to undergo the robotic revolution, but to help drive it.

For the French robotics sector, 2026 could mark the beginning of a new strategic cycle a cycle where technological performance, energy efficiency, and artificial intelligence converge toward more autonomous, sustainable, and competitive robotics.

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In 2026, a new milestone is reached: AI is moving out of screens to act in the real world. This marks the emergence of what is now called physical AI.

Definition: What is Physical AI?

Physical AI refers to artificial intelligence systems capable of perceiving, deciding, and acting in a real, material environment in real time. Unlike purely software-based AI, it is directly connected to sensors, actuators, and physical machines.

In robotics, this means AI no longer just analyzes or predicts:
it directly controls movements, adapts its behavior to the environment, corrects its actions, and interacts with humans, objects, or other machines.

We then speak of a complete loop:

• Perception (vision, sensors, signals)

• Understanding (AI models)

• Decision (reasoning, arbitration)

• Action (motors, arms, locomotion)

• Continuous feedback

Physical AI no longer just
calculates it acts in the
real world.

How Does Physical AI Change Robotics?

For a long time, industrial robots operated according to deterministic programs: a specific task, a fixed scenario, a controlled environment.
Physical AI introduces a major shift: dynamic adaptation.

A robot equipped with physical AI can:

• Handle unstructured environments

• Adapt to unforeseen variations

• Learn from mistakes

• Adjust movements in real time

• Cooperate with other robots or humans

This paves the way for much more versatile robots, capable of leaving ultra-controlled environments to operate in complex, changing real-world industrial contexts.

Technological Building Blocks of Physical AI

It relies on the convergence of several key technologies:

• Advanced sensors
  3D vision, lidar, RGB-D cameras, force sensors, tactile sensors, acoustic sensors…
  They allow the robot to perceive its environment in detail.

• Artificial intelligence models
  Neural networks, multimodal models, decision models, sometimes even LLM-type models adapted for action.
  These interpret the situation and choose relevant actions.

• Actuators and mechatronics
  Motors, articulated arms, grippers, locomotion systems.
  This is what allows AI to turn decisions into physical action.

• Embedded computing and real-time processing
  Physical AI requires decisions in milliseconds.
  This demands powerful embedded architectures, often hybrid (edge + cloud).

Physical AI, Industrial and Humanoid Robots

Physical AI is now at the heart of two major robotic evolutions.

In industry:

It enables the transition from classic automation to autonomous systems:

• Robots capable of adjusting their trajectory

• Self-optimizing production lines

• Intelligent and corrective maintenance

• Smooth human–machine interaction

Robots are no longer just executors; they are systems capable of arbitration.

In humanoid robots:

Physical AI is essential for:

• Balance

• Handling varied objects

• Understanding human gestures

• Navigating human-designed environments

Without physical AI, a humanoid remains a mechanical demo.
With it, it becomes an operational actor.

Why Physical AI Becomes Strategic

Several factors explain the current acceleration:

• Maturity of AI models

• Lower sensor costs

• Powerful embedded computing

• Industrial pressure on productivity

• Shortage of skilled labor

• Need to automate complex tasks

In 2026, physical AI is no longer a lab topic.
It becomes an industrial competitive advantage.

Limits and Challenges

Despite its potential, physical AI still faces major challenges:

• Safety and reliability of decisions

• Certification of autonomous systems

• Integration costs

• Dependence on data

• Human acceptance in work environments

AI that acts physically carries new responsibilities.
A software error can now have material consequences.

In a warehouse or factory,
physical AI becomes the
operational conductor.

In a European automotive production plant, an autonomous mobile robot equipped with physical AI was deployed to maintain critical assembly lines. Using vibration sensors, high-resolution cameras, and force sensors, the robot continuously patrols the plant, detects anomalies invisible to the human eye (micro-vibrations, abnormal heating, mechanical misalignments), and analyzes them in real time using embedded AI models.

When a risk of failure is identified, the system does not just alert: it adapts its behavior, temporarily slows the affected line, adjusts operational parameters, and can even perform simple corrective actions such as tightening a component or recalibrating a sensor. Result: significantly fewer unplanned stoppages, lower maintenance costs, and better production continuity.

This perfectly illustrates the difference between analytic AI and physical AI: here, AI perceives, decides, and acts directly on the industrial system without immediate human intervention.

Concrete Example: Physical AI in a Logistics Warehouse

In a high-volume e-commerce logistics center, physical AI orchestrates a fleet of autonomous mobile robots for order preparation. Each robot is equipped with cameras, depth sensors, and load sensors, allowing it to navigate partially cluttered aisles, identify packages of varying sizes, and interact safely with human operators.

The AI does not merely follow a predefined route: it analyzes congestion, order priorities, stock availability, and the state of other robots in real time. Based on these parameters, the system dynamically adjusts trajectories, redistributes tasks, and changes the order of order picking. When an unexpected event occurs misplaced pallet, damaged package, sudden order spike the physical AI immediately adapts robot behavior without stopping the flow.

This deployment increases throughput, reduces human error, and handles peaks in demand without massive hiring. Physical AI becomes the operational conductor of the warehouse, transforming logistics into a self-adaptive system.

Toward a New Era of Robotics

Physical AI marks a turning point:
robotics no longer just executes it understands, decides, and acts.

We are entering an era where robots become fully intelligent systems, capable of interacting autonomously and contextually with the real world.
This transformation redefines factories, warehouses, logistics, maintenance, and ultimately our very relationship with machines.

Physical AI is not just another trend.
It is the technological foundation of robotics for the next decade.

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In recent years, this separation has begun to fade. By 2026, the convergence of robots and artificial intelligence is no longer limited to demonstrators or pilot projects; it is fundamentally redefining the very nature of the factory. This convergence signals a structural transformation of the industrial model, far beyond traditional automation.

From Rigid Automation to Contextual Autonomy

Traditional factories operated on a simple principle: production lines optimized for a specific product, with robots programmed for repetitive, predictable tasks. This model has reached its limits in the face of rising customization, market volatility, and labor pressures.

The integration of AI radically changes the game. Robots no longer just follow fixed trajectories; they begin to perceive, interpret, and adapt to their environment.

Cameras, force sensors, 3D vision, real-time analytics AI enables robots to handle part variations, logistical uncertainties, human interactions, or changes in production speed. The factory becomes less rigid, more responsive, and more tolerant of the unexpected.

The convergence of robots
and artificial intelligence does
not just modernize the factory
it redefines its very nature.

Emergence of the Robot as an Industrial Agent

A strong signal of the factory of the future is the changing status of the robot. It is no longer just an automated tool machine but an industrial agent with a degree of decision-making autonomy.

In practice, this means that a robot can:

• Adjust its actions based on context

• Detect anomalies or quality deviations

• Halt a process if it identifies a risk

• Cooperate with other robots or human operators

This evolution relies on specialized AI models capable of operating in constrained environments with high safety and reliability requirements. The intelligence is not general but targeted toward specific industrial applications.

A Factory Driven by Real-Time Data

The convergence of robots and AI also reveals a profound shift in industrial management. The factory of the future is not just automated; it is data-native.

Every robot becomes a data collection point: movements, forces, cycle times, quality, incidents, energy consumption. Continuously analyzed by AI systems, this data drives:

• Production flow optimization

• Predictive maintenance

• Energy management

• Continuous process improvement

The factory stops being a collection of isolated machines and becomes a living system capable of learning from its own operation.

From Programming to Industrial Reasoning

AI also changes how robots are designed and deployed. Whereas traditional programming required detailed, fixed instructions, modern approaches favor:

• Learning by demonstration

• Goal-oriented configuration

• Dynamic adaptation to constraints

Engineers no longer dictate every move but define frameworks, rules, and priorities. The robot then selects the best possible action within that framework. This approach reduces commissioning times and opens automation to tasks previously considered too complex or variable.

Human-Robot Collaboration as an Industrial Norm

The factory of the future, revealed by robot–AI convergence, is not human-free. On the contrary, it relies on structured collaboration between operators and intelligent machines.

Robots handle arduous, repetitive, or dangerous tasks, while humans focus on:

• Supervision

• Problem-solving

• Quality control

• Process improvement

• Strategic decision-making

AI plays a key role in securing interactions, anticipating behaviors, and adapting robots to human presence. The line between automation and collaboration becomes fluid.

Flexibility, Resilience, and Reshoring

The combination of robotics and AI also reflects a geopolitical transformation of industry. Factories of the future are designed to be more flexible and resilient.

Capable of quickly switching production, adapting to fluctuating volumes, and reducing dependence on scarce labor, they facilitate:

• Partial reshoring of certain production lines

• Securing supply chains

• Reducing industrial risks

Intelligent automation no longer focuses solely on productivity but on operational continuity in an unstable world.

Persistent Technological and Human Challenges

This vision of the factory of the future does not obscure the major challenges that remain. Robot–AI convergence raises complex issues:

• Industrial system cybersecurity

• Algorithm reliability in critical situations

• Accountability in case of incidents

• Social acceptance and job transformation

• Upskilling teams

AI introduces an element of non-determinism into environments historically designed for complete control. This requires new approaches to validation, certification, and industrial governance.

Industrial intelligence is not
general; it is specialized, constrained,
and purpose-driven.

A Gradual but Irreversible Transformation

Contrary to futuristic rhetoric, the factory of the future will not appear overnight. It is built in successive layers: smarter robots, more interconnected systems, better data-informed decisions.

But the direction is clear. The convergence of robots and AI does more than improve the existing system; it changes the very nature of the industrial system. Factory becomes adaptive, learning, and capable of absorbing uncertainty.

The Factory of the Future as a Collective Intelligent System

What robot–AI convergence reveals is not a fully automated or dehumanized factory, but a collective intelligent system in which machines, software, and humans cooperate in real time.

In this model, value is no longer measured solely by speed or production volume but by the ability to adapt, anticipate, and make responsible decisions.

The factory of the future will not be defined by the number of robots installed but by the quality of the intelligence orchestrating the system. On this front, the convergence of robotics and AI marks a major industrial turning point, whose effects are only beginning to be felt.

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While Western and Japanese robotics programs often prioritize research excellence or long-term breakthroughs, Chinese industrial players are approaching humanoid robots with a distinctly different mindset: build fast, deploy early, iterate in production environments. The result is a rapid transition from experimental prototypes to real-world industrial trials.

This shift does not mean humanoid robots are suddenly replacing human workers at scale. It does mean they are crossing a critical threshold from concept to industrial asset.

A Pragmatic View of the Humanoid Robot

In China, humanoid robots are not framed as futuristic replicas of humans. They are treated as general-purpose industrial platforms designed to operate in environments built for people.

Factories, warehouses, power plants, logistics hubs, and public infrastructure were designed around human dimensions stairs, doors, workbenches, corridors. The humanoid form factor is therefore seen not as an aesthetic choice, but as a practical solution to avoid costly infrastructure redesign.

Chinese manufacturers focus on what humanoid robots can do today: basic manipulation, mobility, inspection, transport, and assistance in semi-structured environments. The emphasis is on usefulness, not perfection.

In robotics, the real breakthrough
is not intelligence it’s
industrialization.

An Industrial Ecosystem Built for Speed

One of China’s strongest advantages lies in its industrial ecosystem. Unlike fragmented supply chains elsewhere, China benefits from a highly integrated hardware manufacturing base.

Key components motors, reducers, batteries, sensors, cameras, embedded computing boards, power electronics are produced domestically or regionally, often within the same industrial clusters. This proximity dramatically reduces development cycles.

When a design flaw appears, components can be revised, tested, and reintegrated in weeks rather than months. Prototyping and pre-series production move quickly, allowing manufacturers to reach industrial readiness far sooner than many global competitors.

Designing Humanoids for Manufacturability

Chinese humanoid robots increasingly reflect a “design-for-manufacturing” philosophy. Rather than pursuing complex, bespoke mechanisms, manufacturers prioritize:

• Modular limb architectures

• Standardized joint actuators

• Simplified mechanical transmissions

• Repeatable assembly processes

• Scalable bill-of-materials structures

This approach mirrors the playbook used in China’s electric vehicle sector: industrialize first, refine later. Humanoid robots are no longer treated as one-off machines, but as platform products capable of evolving through generations.

The goal is not to create the most advanced humanoid robot on paper, but the one that can be built, serviced, and deployed at scale.

AI as an Enabler, Not a Marketing Tool

Artificial intelligence plays a central role but in a restrained and functional way. Rather than chasing artificial general intelligence, Chinese developers focus on task-oriented autonomy.

Key areas of AI deployment include:

• Visual perception for object recognition and localization

• Learning-by-demonstration for industrial gestures

• Balance and motion planning in dynamic environments

• Basic decision-making under uncertainty

This pragmatic use of AI enables humanoid robots to operate effectively without requiring excessive computational overhead or unrealistic performance expectations. Reliability matters more than sophistication.

As a result, these systems may appear less impressive in demos but they perform more consistently in real industrial conditions.

Early Industrial Use Cases Are Already Emerging

Several Chinese manufacturers are already testing humanoid robots in real operational settings. These deployments remain limited in scope but are significant in intent.

Current use cases include:

• Internal logistics within large factories

• Transport of light components and containers

• Inspection of equipment and facilities

• Repetitive handling tasks in controlled zones

• Operations during night shifts or low-staffed periods

In most cases, humanoid robots are positioned as support systems, not replacements. They complement existing automation and human labor, filling gaps where flexibility is required.

Government Support Focused on Deployment, Not Just R&D

China’s public sector plays a critical but often misunderstood role. Rather than concentrating solely on research funding, government policies emphasize industrial validation and market creation.

Pilot zones, test facilities, procurement programs, and public-private partnerships provide real environments where humanoid robots can be evaluated under operational constraints. This reduces risk for manufacturers and accelerates learning cycles.

The domestic market serves as a proving ground before international expansion allowing Chinese firms to refine products at scale before competing globally.

How China Differs from the US, Japan, and Europe

The Chinese approach contrasts sharply with other major robotics regions.

The United States excels in AI software, simulation, and high-level robotics intelligence but often struggles with large-scale hardware industrialization.

Japan remains a global leader in precision mechanics and reliability, yet tends to move cautiously when it comes to radically new form factors.

Europe emphasizes safety, regulation, and industrial integration, which can slow down early experimentation.

China’s advantage lies in its willingness to deploy early, accept imperfections, and improve through use. Speed, cost control, and manufacturing scale form a powerful combination.

Speed to deployment is the
new competitive edge in
robotics.

Challenges Still Limit Mass Adoption

Despite rapid progress, significant hurdles remain. Humanoid robots Chinese or otherwise still face:

• Limited battery life

• Long-term reliability concerns

• Safety certification challenges

• Workforce acceptance issues

• Integration complexity with existing systems

Humanoid robots are not yet plug-and-play solutions. Their industrial adoption will depend on continued improvements in robustness, maintenance, and total cost of ownership.

Export markets also impose stricter compliance and regulatory requirements, particularly in Europe and North America.

A New Phase in Industrial Robotics

What is unfolding in China is not a sudden breakthrough, but a structural shift in strategy. Humanoid robots are moving from speculative R&D projects to industrial products subjected to market discipline.

This transition mirrors earlier phases of robotics evolution, where initial skepticism eventually gave way to widespread adoption once cost, reliability, and usefulness aligned.

Humanoid robots may not dominate factories tomorrow but they are no longer science experiments.

When Execution Becomes the Competitive Edge

The journey from prototype to factory floor defines the next chapter of humanoid robotics. Chinese industrial players are not claiming to have solved every technical challenge. Instead, they are proving something arguably more important: humanoid robots can be built, deployed, and improved in real industrial contexts.

In this race, technological brilliance alone is not enough. Execution speed, manufacturing capability, and learning through deployment are becoming decisive advantages.

For the global robotics industry, the lesson is clear. The future of humanoid robots will not be decided only in laboratories but on factory floors, where practicality ultimately outweighs promise.

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This year marks a clear break from that trajectory. In 2026, robotics is no longer advancing incrementally. It is changing scale.

This shift is not driven by a single breakthrough, but by the convergence of artificial intelligence, embedded computing, simulation, labor shortages, and platform standardization. Robotics is moving beyond factory cells and entering the real world warehouses, hospitals, farms, construction sites, and public spaces.

For Robot Magazine, 2026 stands out as a pivotal year, comparable to the rise of cloud computing in the 2010s or the smartphone revolution in the early 2000s.

A Technology Long Limited by Its Own Constraints

Until recently, most industrial robots operated under strict conditions:

• fixed environments

• repetitive tasks

• deterministic programming

• limited perception

• high integration costs

Robots excelled at repetition but struggled with variability. A change in lighting, object shape, workflow, or layout often required costly reprogramming or system downtime.

This rigidity kept robotics largely confined to large-scale manufacturing, particularly automotive and electronics. Small and medium-sized enterprises, logistics, healthcare, agriculture, and construction remained largely manual.

The promise of robotics was clear but its reach was narrow.

What is happening in robotics is not
a technological miracle, but the
convergence of AI, computing power,
economics, and real-world constraints.

Artificial Intelligence Turns Robots into Decision-Making Systems

The first driver behind robotics’ change of scale is the maturation of artificial intelligence.

Modern robots are no longer just executing predefined trajectories. They are increasingly capable of:

• perceiving complex environments

• interpreting visual and sensor data

• understanding high-level instructions

• planning actions dynamically

• adapting to uncertainty

Multimodal AI models combining vision, language, force feedback, and motion allow robots to reason within constraints. This does not mean robots “think” like humans, but they can now make context-aware decisions instead of blindly following scripts.

This shift transforms robots from automated machines into physical AI agents, capable of operating in environments that were previously considered too unpredictable.

Embedded Computing Redefines Autonomy

Equally important is the rise of high-performance embedded computing.

Thanks to new generations of processors and AI accelerators, robots can now perform complex inference directly on board. This enables:

• real-time perception and decision-making

• low-latency control loops

• reduced dependence on cloud connectivity

• improved reliability and safety

• better data sovereignty

Robots no longer need external PCs or centralized servers to function intelligently. They have become edge systems, capable of operating autonomously even in remote or critical environments.

This is a fundamental shift. Intelligence is no longer centralized it is distributed across fleets of machines.

Robots Enter Unstructured, Human-Centric Environments

Robotics is changing scale because robots are no longer designed only for structured spaces.

Alongside traditional industrial arms, we now see:

• autonomous mobile robots navigating busy warehouses

• collaborative robots working safely next to humans

• service robots operating in hospitals and hotels

• agricultural robots adapting to natural terrain

• inspection and maintenance robots in infrastructure

• humanoid robots designed for human-built environments

The defining feature of these robots is not their form factor, but their adaptability. They operate in spaces built for humans, not redesigned for machines.

This marks a major philosophical shift: instead of forcing the world to adapt to robots, robots are adapting to the world.

Labor Shortages Accelerate Adoption

Beyond technology, economics plays a decisive role.

Across industries and regions, companies face:

• aging workforces

• labor shortages in physically demanding jobs

• high turnover rates

• declining interest in repetitive or hazardous tasks

In logistics, agriculture, manufacturing, healthcare, and sanitation, automation is no longer about productivity alone. It is about operational continuity.

Robotics becomes a necessity rather than an optimization tool. In many cases, robots are not replacing workers they are filling positions that no longer attract human labor.

This structural shift is one of the strongest accelerators of large-scale robotic deployment.

Robotics may be less visible than
consumer AI, but its transformation
is deeper, slower and far more structural.

Platform Standardization Fuels the Ecosystem

Another key factor behind robotics’ change of scale is platform convergence.

The robotics sector has long been fragmented, with proprietary hardware, software, and integration methods. That fragmentation is now giving way to:

• shared operating systems and middleware

• common simulation and development tools

• standardized interfaces between robots and IT systems

• cloud-edge hybrid architectures

This standardization reduces development costs, shortens deployment cycles, and lowers barriers to entry for new players. It also allows skills, software, and best practices to transfer across platforms.

Robotics is beginning to resemble modern computing: modular, updateable, and ecosystem-driven.

From Individual Robots to Robotic Infrastructure

Perhaps the most important shift is conceptual.

Robots are no longer treated as isolated machines. Companies now think in terms of:

• robot fleets

• centralized supervision

• orchestration layers

• predictive maintenance

• digital twins

• continuous learning systems

A robot deployed today improves tomorrow through shared data, software updates, and collective learning across fleets. Value increasingly lies in software, data, and integration not just hardware.

Robotics is becoming an infrastructure layer, similar to IT systems or cloud platforms.

Challenges Remain and They Are Significant

Despite this change of scale, robotics still faces real obstacles:

• high upfront investment costs

• cybersecurity risks

• regulatory and safety compliance

• ethical and social concerns

• workforce adaptation and training

Technology is advancing faster than organizational readiness. Deployment success depends not only on engineering, but also on change management, regulation, and human acceptance.

The challenge is no longer whether robots can perform tasks but how societies and industries integrate them responsibly.

2026: A Pivotal Year

What makes this year different is not hype, but alignment.

Technology, economics, and societal needs are converging. Robotics is expanding beyond pilot projects and isolated use cases into scalable, repeatable deployments across sectors.

Manufacturing, logistics, healthcare, agriculture, energy, and public services are all being reshaped by robotic systems that are more autonomous, flexible, and intelligent than ever before.

This shift is irreversible.

A Quiet but Structural Transformation

Robotics may not dominate headlines like consumer AI, but its impact is deeper and more structural. The change of scale we are witnessing this year reflects the maturation of robotics as a foundational technology.

Robots are no longer futuristic promises. They are becoming reliable, adaptable tools embedded in everyday operations.

For Robot Magazine, 2026 marks the moment when robotics transitions from a specialized industrial solution to a cross-sector infrastructure, reshaping how work is performed, how services are delivered, and how physical systems interact with digital intelligence.

The key question is no longer if robotics will scale but how quickly industries and societies will adapt to this new reality.

FAQ – Why Robotics Is Changing Scale in 2026

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So, should we see these robots as a technologically alluring trend doomed to fade, or are we witnessing the emergence of a new industrial revolution, driven by AI, advanced robotics, and a global labor shortage?

Robot Magazine investigated this trend by analyzing recent advances, persistent limitations, and the industrial stakes that will determine the future of humanoids.

Why focus on humanoids now?

Five years ago, the question barely arose. Humanoids lacked:

• Energy autonomy

• Precision in manipulation

• Onboard computing power

• Stability in locomotion

• The ability to interpret the real world

But several developments have converged over the past three years:

1. The arrival of multimodal models capable of reasoning and manipulating
   Companies like OpenAI, Figure AI, and Tesla have introduced AI systems combining vision, language, planning, and action. These AI systems can explain why they perform a task, propose solutions, or adjust movements based on context.

2. The dramatic drop in computing costs
   NVIDIA platforms (Jetson, Orin, and now Blackwell) have lowered AI inference costs, enabling complex models to run on mobile robots.

3. Massive simulation
   Digital twins (Omniverse, Isaac Sim, Mujoco, Unreal Engine) allow humanoids to be trained in millions of virtual scenarios before entering the real world.

4. A structural labor shortage
   Logistics, healthcare, agriculture, hospitality, recycling industries are looking for solutions to difficult, repetitive, or physically demanding tasks.

These factors have shifted humanoids from an imagined future to an operational one.

Humanoid robots are not designed
to replace humans but to take over
tasks that no one wants or can perform
anymore.

What can humanoids really do today?

Public demonstrations sometimes make humanoids appear clumsy. Yet in industrial settings, their real capabilities are much more impressive.

1. Versatile manipulation
   Most new humanoids can:

   • Grasp fragile objects

   • Screw, unscrew, assemble

   • Sort irregular parts

   • Adjust movements using tactile feedback

   Robotic hands are now among the most advanced components, sometimes comparable to human hands.

2. Dynamic locomotion
   Recent bipedal robots can:

   • Climb stairs

   • Overcome obstacles

   • Navigate unstructured environments

   • Avoid people in real time

   The goal is no longer just to walk, but to walk usefully.

3. Real-time 3D perception
   They use combinations of:

   • LiDAR

   • Stereo cameras

   • IMUs

   • RGB-D cameras

   • AI models for segmentation and detection

   Result: they understand their environment much more richly.

4. Collaborative work
   Humanoids can interact with humans, recognize gestures, follow voice commands, or coordinate with other robots.

Why are industries finally taking notice?

The main advantage of humanoids is not technical but economic and logistical.

1. A robot that integrates into human environments
   Unlike industrial arms, AGVs, or AMRs, a humanoid:

   • Doesn’t need a redesigned workspace

   • Doesn’t require full-line automation

   • Works with existing tools

   • Can replace or assist an operator in a specific task

   It fits into human infrastructure rather than imposing it.

2. Unprecedented versatility
   A humanoid can change roles like a temporary worker:

   • Morning: handling

   • Afternoon: sorting and inspection

   • Evening: packaging

   This flexibility reduces the ROI threshold for investment.

3. A response to labor shortages
   In logistics, hospitality, or industry, some tasks simply have no candidates. Humanoids don’t replace skilled jobs but fill unstaffed positions.

The limits: not everything is ready for industrialization

Enthusiasm is real, but obstacles remain.

1. Cost
   A humanoid costs €60,000–€150,000 today, not including maintenance.

2. Energy autonomy
   Most operate 1.5–3 hours per charge, requiring:

   • Extra batteries

   • Quick-swap stations

   • Smart cycling

3. Robustness
   Bipedal robots remain vulnerable to shocks, dust, temperature changes, and extreme humidity compared to industrial arms.

4. Regulations
   Humanoids must comply with standards:

   • CE

   • ISO 10218

   • ISO 15066

   • ISO 13482 for personal robots

   Full safety certification remains complex.

Fad or revolution? Both, actually

A clear media effect
Viral videos of robots running or folding laundry generate buzz but can misrepresent real industrial contexts. Humanoids spark imagination, leading to both hype and skepticism.

But a structural revolution is underway
The real transformation isn’t in the humanoid shape but in:

• Context-aware robots

• Able to perceive

• Understand tasks

• Adapt to variability

• Collaborate with humans

Humanoids are a vehicle for a deeper revolution: the rise of intelligent, versatile robots.

Industry adopts humanoids not out
of fascination, but necessity: available
human labor is no longer enough.

2026–2032: What will really change

1. Massive pilot deployments
   Fleets of 100–1,000 humanoids will be deployed in logistics and modular factories.

2. Industrial standardization
   Humanoids will enter CE and ISO norms with adapted certification procedures.

3. Specialized humanoids
   Expect:

   • Handling humanoids

   • Quality inspection humanoids

   • Service humanoids

   • Agricultural humanoids

4. Integration with cloud/AI ecosystems
   Humanoids will become physical agents in multicloud architectures (Microsoft, Google, AWS) with:

   • Digital twins

   • Remote supervision

   • Shared global learning

   • Continuous optimization

The humanoid form is not a gimmick it’s an emerging standard.

Humanoid robots aren’t a universal solution but offer compatibility with human-built environments.

The question isn’t whether they will replace industrial arms or AGVs but whether they will become an additional tool for non-standardized, varied, and unpredictable tasks.

In 2026, everything indicates that humanoids are both:

• A media-fueled trend

• The start of a deep industrial revolution

Reality lies in between: they won’t dominate all workshops but will become essential where versatility, adaptability, and perception matter.

Robot Magazine will continue tracking this transformation, as humanoids’ arrival in industry could redefine the very concept of automated work.

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By unveiling its MOTOMAN NEXT platform, Yaskawa marks an important milestone in industrial automation. The Japanese company, already well established in robotics and automation, introduces an architecture designed to bridge two worlds long kept apart: traditional industrial automation (OT) and advanced digital technologies (IT).
The goal? To make robots capable not only of executing tasks, but also of observing, understanding, and adapting in a global context marked by labor shortages and the rise of complex tasks demanding automation.

An All-in-One Platform Unifying Hardware, Software, and Embedded Intelligence

According to the press release, MOTOMAN NEXT is presented as a complete solution in which “robot, controller, software, and engineering tools” are grouped into a single ecosystem.

The core of the system relies on two main units:

• RCU (Robot Control Unit): the traditional robot control unit.

• ACU (Autonomous Control Unit): a new module dedicated to intelligent functions, based on NVIDIA Jetson Orin NX-Edge, integrating CPU + GPU, running Linux with Docker support.

This hybrid architecture paves the way for robotics that depends less on external computers, by integrating directly into the controller functions usually assigned to IT stations: image processing, AI, vision, planning, sensor management…
A rare approach in an industry typically fragmented between PLC logic and PC-based software.

Yaskawa also provides pre-installed APIs for motion control, planning, 2D/3D vision, force control, and image processing via HALCON ©.

A ROS2 interface connected via gRPC further opens the platform to the open-source robotics community.

With MOTOMAN NEXT, Yaskawa is no
longer just programming robots: it is
teaching them to perceive, understand,
and adapt.

Toward a Fusion of OT and IT Worlds

A section of the press release highlights a major problem in modern industrial robotics: the gap between the languages, tools, and programming philosophies of OT engineers and IT developers.
OT engineers work with PLCs, while IT developers use high-level code (Python, C++), often generated on a PC then injected into the robot through interfaces that can be limited or prone to latency.

MOTOMAN NEXT attempts to bridge this gap by bringing these logics together in a single controller capable of executing standard Docker modules while optimizing classic industrial robotics functions (kinematics, speeds, singularities) for Yaskawa hardware.

This integration avoids historical issues such as:

• Complex waypoints
• Unstable dynamic behavior
• Dependency on external PCs
• Difficult integration of vision or sensors

The idea is to offer IT developers the experience of a seasoned robot programmer while ensuring that advanced applications vision, AI, perception run directly at the controller level.

Robots That Perceive, Understand, and Produce

This change of methodology is described as a transition from the paradigm “Code and produce” to “Perceive and produce”.

Traditional robots operate in deterministic environments, with standardized parts and preprogrammed sequences. But when variability increases formats, batch sizes, operation order classical programming reaches its limits.

With the combination of sensors + AI, MOTOMAN NEXT targets applications previously reserved for human workers:

• Flexible assembly
• Sorting and handling of unstructured flows
• Variable loading/unloading
• Random cleaning and manipulation
• Harvesting and packaging in changing environments

Potentially impacted sectors go far beyond heavy industry: logistics, healthcare, construction, food service, recycling all industries currently suffering from labor shortages.

A New Line of Industrial Robots and Cobots

The MOTOMAN NEXT line NEX series covers payloads from 4 kg to 35 kg, with high-inertia servomotors designed to ensure perfect alignment between the digital model and real operation, facilitating simulation and virtual optimization.

Yaskawa also introduces new cobots (NHC series: 12 and 30 kg), equipped with an embedded RGB-D camera enabling direct perception from the robot and adaptive reactions in real time.
This “bodycam” opens the door to more natural interactions between robot and environment.

Digital Twin and Advanced Engineering

The MOTOMAN NEXT package includes the YNX Robot Simulator, a professional simulation tool enabling:

• Virtual cell creation
• Trajectory testing
• AI scenario validation
• Optimization before real deployment

This digital twin is key to reducing development risks and supporting the industrialization of intelligent robotic applications.

For advanced teams, Yaskawa also announces official support for NVIDIA’s tools: Isaac Sim and Cumotion.

Robotic autonomy is entering a new era
where the robot is no longer dependent
on an external PC to think.

A Simplified Operator Interface

On the operator side, Yaskawa focuses on continuity: the Android Smart Pendant tablet remains the main interface. Sequences can be created in block-based language using icons representing functions (planning, vision recognition…), making programming more intuitive.

The platform is also prepared for next-gen LLM-based interactions: voice commands, gestures, AR (augmented reality) assistance.

Automatic Trajectory Planning: A Key Feature

Among the highlighted technical features, automatic collision-free trajectory planning (Path Planning) plays a central role.
A simple “MOVAUTO” command would allow the robot to move from point A to point B while automatically avoiding surrounding obstacles.

This planning relies on 3D modeling:

• Static: cell model
• Dynamic: real-time updates from the camera

This is essential for AI applications and evolving Industry 4.0 environments.

A Strategy Based on Partnerships and Real-World Use Cases

Yaskawa emphasizes that the success of AI/robotics projects depends on close cooperation between:

• End users
• Integrators
• Technology partners
• Yaskawa

The goal is to go beyond prototypes and achieve solutions that are fully industrialized and capable of continuous production.

A Significant Breakthrough in a Changing Robotics Landscape

With MOTOMAN NEXT, Yaskawa is not simply launching a new robot line: it is proposing a complete re-architecture of the robotics development cycle.
The OT/IT fusion, native integration of AI and vision, advanced simulation, embedded perception, and intelligent planning address today’s challenges: automating complex tasks, making robots more autonomous, and compensating for labor shortages across many sectors.

If the platform delivers on its promises in real-world deployments, it could redefine industrial robotics standards where the boundary between mechanical execution and software intelligence is progressively disappearing.

FAQ – Yaskawa MOTOMAN NEXT

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