Physical AI – artificial intelligence embedded in systems that interact with and manipulate the physical world – dominated the conversation at Hannover Messe 2026 in Hanover, Germany, as manufacturers and robotics companies from across Europe and China demonstrated industrial-grade robots to an audience of more than 3,000 exhibitors. For Germany’s industrial sector, which has faced compounding pressures from high energy costs, weak demand, and skilled labor shortages, the event served as both a showcase and a challenge.

German Chancellor Friedrich Merz attended the fair and visited the stand of Agile Robots, a Munich-based startup founded by Chinese entrepreneur Zhaopeng Chen. In a speech at the event, Merz called for AI to be embedded in key industrial sectors and specifically in small- and medium-sized enterprises, describing it as the path toward “industrial added value and high-quality jobs.”

Agile Robots and the Industrial Focus

Agile Robots demonstrated a humanoid robot performing precision manipulation tasks – opening boxes, handling tools – at the fair. CEO Rory Sexton said the company plans to begin fitting out German factories, with a particular focus on the automotive sector, from next year.

Sexton drew a deliberate contrast between demonstration-oriented robotics and industrial deployment. Rather than showcasing martial arts or dance routines, Agile Robots is targeting value-added manufacturing tasks such as electronic wiring in cars and phone assembly. “We’ll soon be able to do what they are doing,” Sexton said of Chinese competitors, while arguing that Germany’s ecosystem of mechanical engineering suppliers and deep automation expertise gives European players structural advantages in industrial AI.

China’s Presence and Germany’s Gap

Chinese manufacturers including Unitree were present at the fair in significant numbers, continuing a pattern from previous years. Merz had witnessed Chinese humanoid robot demonstrations during a visit to China in February – including displays of robots performing kung fu and boxing – and acknowledged the pace of Chinese development publicly.

The competitive gap in humanoid robot manufacturing is widely recognized. A survey by German digital business association Bitkom found that 58% of industrial firms believe humanoid robots could help address skilled labor shortages – a number that reflects both the scale of the problem and the industry’s openness to robotic solutions.

Data Advantage and Its Limits

Antonio Krueger, head of the German Research Centre for Artificial Intelligence, argued that Germany holds a data advantage that is frequently underestimated. “This is something we have at a level of quality far superior to the United States or China,” he said, referring to the depth of industrial operational data accumulated across German factories over decades.

Critics counter that this data remains fragmented and underutilized, with no coordinated national strategy to aggregate it into AI training pipelines that could accelerate development at scale. The gap between data availability and data accessibility is one of the central structural challenges German industrial AI faces.

Skepticism was also present at the fair. Jochen Heinz, an executive at German factory machinery manufacturer SW Machines, cautioned that AI systems can produce errors in industrial settings – providing misleading repair instructions or generating false fault detections. “With AI, I also see the dark side of the force,” he said. For manufacturers operating precision equipment where errors carry real costs, those concerns are not easily dismissed.

A&K Robotics, a Canadian company building autonomous mobility infrastructure for airports, has closed a CAD $8 million Series A investment. The round was led by BDC Capital’s Industrial Innovation Venture Fund and Vantage Futures, the corporate venture arm of Vantage Group, a global airport and transportation infrastructure operator. Additional investors include RiSC Capital, Grep VC, Nimbus Synergies, and Dan Gelbart, co-founder of Creo and Kardium.

The funding will support A&K’s transition from pilot programs to permanent deployments, the expansion of production capacity, and the acceleration of adoption across major airport networks globally.

The Problem and the Product

Roughly 17% of the global population lives with mobility challenges, and requests for airport assistance are growing 10-15% annually – a rate that consistently outpaces overall passenger growth. Airports face a structural gap between rising accessibility demand and the labor available to address it.

A&K’s response is Cruz, a self-driving mobility pod purpose-built for dense indoor environments. Passengers select a destination and the vehicle navigates autonomously using onboard sensors and AI, dynamically adjusting its path to move safely through crowds. The system is designed for continuous operation, enabling airports to deliver consistent accessible mobility without scaling headcount proportionally.

Cruz is powered by the company’s Kinesos AI platform, which A&K describes as a system for socially intelligent autonomy – enabling vehicles to move naturally through dynamic, unpredictable human environments rather than following fixed routes or requiring controlled corridors.

Current Deployments

Cruz is already operating in live airport environments. Deployed customers include Vancouver International Airport, ranked Best Airport in North America 15 times by aviation research firm Skytrax, and Madrid-Barajas Airport, operated by Aena – the world’s largest airport operator by passenger volume, serving more than 380 million travelers annually. The combination of a North American and a major European reference deployment gives A&K a geographic footprint that supports broader enterprise sales conversations.

“While others focus on roads, we’re tackling the harder problem – navigating dense, unpredictable airport crowds,” said Jessica Yip, COO of A&K Robotics. “Autonomous mobility is already standard in warehouses. We are bringing it into the most complex indoor environments.”

Expanding Manufacturing Capacity

The Series A will fund the establishment of a third facility in Surrey, British Columbia, expanding into a 55,000 square-foot site at Manterra Technologies. The move is expected to increase manufacturing output from dozens to hundreds of autonomous vehicles per year. A new rapid prototyping and R&D facility has also been established to accelerate iteration on deployment-ready systems.

The manufacturing expansion reflects a deliberate shift in focus from technology validation to production scale. Airport operators evaluating permanent deployments require supply chain certainty that pilot-stage companies cannot always provide – the new facility is designed to address that constraint directly.

“Their ability to deploy in dense, high-traffic airport environments positions them as a key partner for operators looking to improve operational efficiency, enhance passenger experience, and scale autonomous mobility across global networks,” said Matthew Handford, Executive Managing Director at Vantage Futures.

Zoomlion Heavy Industry Science and Technology, the Hong Kong-listed Chinese industrial machinery manufacturer, made the global debut of Robot Ops at Hannover Messe 2026 this week. The platform is a full-stack embodied intelligence operating system designed to standardize the development and deployment of robots across industrial, logistics, construction machinery, and autonomous driving applications.

The launch positions Zoomlion – better known for cranes, concrete equipment, and agricultural machinery – as an active participant in the industrial AI software layer, not just a hardware operator. The company is exhibiting at the show alongside Amazon Web Services and is participating in the China Pavilion’s Invest in China launch ceremony.

What Robot Ops Does

Robot Ops is built around an engineering concept the company describes as “Data, Software, and Agents,” integrating three operational disciplines – DevOps, DataOps, and AgentOps – into a unified platform. The system covers the full lifecycle of robot deployment: data collection, model training, simulation verification, application development, and ongoing deployment maintenance.

The platform comprises four modules covering basic development tools, imitation learning, reinforcement learning, and task orchestration. Zoomlion says the system improves closed-loop iteration efficiency by more than 50% and is designed to lower the technical barrier for organizations building and deploying robotic systems at scale.

The platform directly targets four challenges that have slowed industrial robotics adoption: high technical barriers to entry, difficulty migrating robot behaviors across different scenarios, data bottlenecks in training pipelines, and the absence of structured lifecycle management tools.

Live Demonstration at Hannover

At the show, Zoomlion is running live multi-robot demonstrations under Robot Ops scheduling. A wheeled humanoid robot and a logistics mobile robot collaborate on a logistics-sorting scenario, with the platform managing algorithm coordination, task orchestration, and on-site execution across both systems simultaneously. The company’s first-generation mass-produced humanoid robot, the Z1, is also on display performing dynamic motion-control demonstrations.

The multi-robot setup is designed to demonstrate Robot Ops’ capacity to coordinate heterogeneous robot types – different hardware, different task profiles – within a single orchestration layer, which is the core engineering claim the platform is built around.

Broader Industrial AI Context

Zoomlion is also presenting its Industry 5.0 intelligent manufacturing solutions at the show, including its Smart Industrial City initiative, which integrates digital twins, intelligent scheduling, industrial AI, and end-to-end logistics automation into manufacturing operations.

The Robot Ops debut reflects a pattern visible across Hannover Messe 2026 more broadly: established industrial companies using the event to announce software and AI platforms that sit above their existing hardware operations, targeting the orchestration and deployment layer rather than competing purely on robot specifications. For Zoomlion, whose core business is heavy construction equipment, the move into embodied intelligence software represents a deliberate effort to participate in the higher-margin, faster-growing segment of the industrial automation stack.

NEURA Robotics, the German cognitive robotics company, and Amazon Web Services have announced a strategic collaboration to accelerate the development and global deployment of physical AI systems. AWS will serve as the primary cloud provider for NEURA’s Neuraverse platform, handling AI training, real-world data processing, and shared intelligence across robot fleets. Amazon will separately explore deploying NEURA robotic systems in select fulfillment centers, providing production-environment data to accelerate the development of new robotic capabilities in logistics and warehouse operations.

The partnership addresses what both companies describe as the central constraint on physical AI progress: the data gap. Unlike large language models trained on trillions of internet-sourced data points, robotic AI systems have access to a fraction of that volume, and the data they need can only be generated through real-world operation.

Three Areas of Collaboration

The agreement spans cloud infrastructure, AI development, and real-world validation. AWS will provide the computational backbone for the Neuraverse, NEURA’s platform for training and sharing robotic intelligence across fleets. NEURA Gym – a purpose-built training environment where robots practice complex tasks in controlled settings alongside high-fidelity simulation – will integrate with Amazon SageMaker to accelerate joint training pipelines across NEURA and partner use cases.

The real-world validation component is the most strategically significant element. Amazon’s fulfillment centers represent one of the most operationally demanding and data-rich environments available for robotic deployment – high throughput, variable product mix, and continuous operation at global scale. Each deployment generates the kind of sensor data, task variety, and edge-case exposure that controlled training environments cannot replicate.

“Physical AI will only reach its full potential if intelligence can be trained, validated, and continuously improved in the real world,” said David Reger, CEO and founder of NEURA Robotics. “With AWS, we gain the infrastructure to scale the Neuraverse globally. With Amazon, we have the opportunity to bring Physical AI into one of the most advanced operational environments in the world.”

The Data Infrastructure Problem

The collaboration is built around a structural challenge that applies across the robotics sector. Simulation can approximate physical environments but cannot fully replicate the variability of real-world conditions – surface irregularities, lighting changes, unexpected object configurations, and human interaction patterns. Continuous feedback loops between simulation and real-world deployment are required to close that gap over time.

AWS’s role is to make those loops faster and more scalable. By running the Neuraverse on cloud infrastructure with global reach, NEURA can distribute trained intelligence across its entire robot fleet in near real time, so improvements derived from one deployment environment propagate across all systems.

Ecosystem and Scale

The AWS partnership extends a network NEURA has been assembling across cloud, semiconductors, and industrial deployment. The company’s existing partners include Kawasaki – one of the ten largest robotics companies globally – alongside Schaeffler, Bosch, and Qualcomm Technologies. The stated goal is to enable millions of cognitive robots by 2030.

NEURA has not disclosed the financial terms of the AWS agreement, the timeline for Amazon fulfillment center deployments, or the specific robotic systems under consideration for those pilots. The fulfillment center component is framed as exploratory, meaning commercial deployment at scale remains contingent on performance outcomes from the initial trials.