FANUC has moved to bind its industrial robots more closely to Nvidia’s simulation and computing stack, in a sign that factory automation is edging toward a more software-defined future. The Japanese group says the partnership will combine its robot hardware and ROBOGUIDE software with Nvidia’s Jetson modules, Isaac Sim and Omniverse libraries.

The immediate appeal is practical in that manufacturers are under pressure to shorten commissioning time, reduce errors and cope with more variable production runs. Digital twins offer a way to test changes before steel is cut or a line is halted. FANUC expects the arrangement will let customers create photorealistic factory replicas, train robots virtually and deploy them with greater speed and flexibility.

Across the industrial world, the race is shifting from merely supplying robots to making them easier to simulate, program and adapt at scale. That matters because physical AI – a phrase now gaining currency increasingly to mean software that can imbue intelligence into machines – promises to merge perception, reasoning and motion in a way that could make robots more useful in automotive plants, logistics hubs and food-processing lines, where variability still frustrates automation.

“Physical AI is the next frontier in industrial automation,” said Mike Cicco, President and CEO, FANUC America. “By collaborating with NVIDIA, we’re giving manufacturers the tools to deploy intelligent robotics faster and align virtual design with real-world production.”

“Manufacturers are increasingly seeking physical AI solutions that bridge the gap between virtual simulation and real-world production to overcome labor shortages and increase operational efficiency,” said Murali Gopalakrishna, general manager of robotics at NVIDIA. “By integrating NVIDIA’s AI and simulation platforms with FANUC’s robotics expertise, we are providing developers with the tools to build and deploy intelligent, adaptable automation at scale.”

The collaboration will advance physical AI — a new paradigm that merges artificial intelligence with physical robotics to enable “machines that see, reason, and act in dynamic environments,” the company said in a press release.

Under the collaboration, FANUC will use NVIDIA AI infrastructure “within its extensive robot portfolio and ROBOGUIDE simulation software,” according to the release. It added that integrating ROBOGUIDE with Isaac Sim and Omniverse would allow manufacturers to “simulate entire production lines with high fidelity, validate workflows, and optimize performance before hardware deployment, reducing commissioning time and cost.”

FANUC also said combining its robots with Jetson and AI-accelerated computing would help machines “adapt to variability, execute complex tasks, and deliver higher throughput”.

Companies such as FANUC face the challenge of continuing to maintain their reputation for reliability, while at the same time ensuring that they don’t fall behind in adapting just the right levels of AI to make their robots genuinely useful in real-world scenarios.

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ShengShu Technology has unveiled Motubrain, a “World Action Model” it says can replace the patchwork of task-specific systems that still define much of robotics today. The Singapore-based company claims the model ranks among the best on two embodied AI benchmarks, WorldArena and RoboTwin 2.0.

The pitch is straightforward: instead of building robots from separate modules for perception, planning and control, ShengShu wants a single architecture that can learn from video, language and action together. That is a familiar ambition in artificial intelligence, but in robotics it remains technically hard, because machines must cope with changing environments, imperfect data and the physical limits of hardware.

The global race in embodied AI is now moving beyond chatbots and image generators toward systems that can interact with the real world, a shift that could reshape manufacturing, logistics, home robotics and industrial automation. For companies and investors, the prize is not merely better models, but software that can generalise across robot types and task settings without being retrained from scratch.

“A true world model must be able to build a unified representation of the real world and predict how it evolves,” said Jun Zhu, ShengShu’s founder. “Video is a critical foundation of that intelligence because it naturally captures time, space, motion, causality, and physical dynamics at scale”.

ShengShu says Motubrain is built on a unified multimodal model and a three-stream mixture-of-transformers design that ties video, action and language into one loop. The company says the model can handle multi-step tasks involving up to 10 atomic actions, well above the two or three actions typical of many robotic systems. It also says the system learned from unlabelled video, task recordings and data from different robot embodiments, reducing dependence on manual annotation.

The company adds that Motubrain is already being used by robotics firms in training programmes spanning industrial, commercial and domestic settings. It has also partnered with Astribot, SimpleAI and Anyverse Dynamics to push the model toward broader deployment. ShengShu, backed by a $293 million Series B led by Alibaba Cloud, is trying to turn its video-model expertise into a foothold in physical AI.

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China’s robots may yet prove less a novelty than a policy tool, according to Morgan Stanley. Analysts at the investment bank predict that robots, and especially humanoids, could help extend the China’s grip on manufacturing, just as electric vehicles did starting about a decade ago.

The bank’s latest note, led by its Chief Economist in Asia, Chetan Ahya, argues that humanoids and other robots will become a key driver of China’s export machine over the next five to 10 years. It repeated its forecast that China’s share of global exports will rise from 15 percent now to 16.5 percent by 2030.

The analysts also said Chinese makers already account for roughly 90 percent of the 13,000 to 16,000 humanoid robots shipped globally last year, with the US and Japan still mostly at prototype stage. China’s annual humanoid sales, they said, could more than double to about 28,000 units this year, more than any other economy.

The bigger story is not about one country’s robot tally, but about industrial policy in an age of tightening supply chains and slower growth. Across the world, governments are treating automation as a strategic asset: a way to offset labour shortages, preserve export competitiveness and defend manufacturing share. China has gone further than most, folding embodied AI and advanced automation into its broader push for “future industries.”

The analysts from Morgan Stanley said, “looking ahead, humanoids and robots will be the next key driver of China’s export machinery over the coming 5 to 10 years.” It added that the industry’s development echoes the rise of electric vehicles a decade ago.

Morgan Stanley’s case rests on scale, state support and a supply chain that already dominates many core components. In its broader humanoid research, published last year, the bank estimated the market could reach $5 trillion by 2050 and said China could have 302.3 million humanoids in use by then, the most of any country. It also warned that the technology remains expensive, imperfect and years away from mass household adoption.

That caution matters. The report is bullish on China’s industrial advantage, but it does not claim humanoids will transform factories overnight. For now, the more immediate lesson is that Beijing appears determined to use robots to deepen an old strength rather than invent a new one. In that sense, the bet is less on science fiction than on familiar industrial discipline, aided by cheap production, abundant suppliers and a state willing to back winners.

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techUK has published its first robotics report, arguing that Britain has the ingredients to lead the next phase of robotics, but needs to move faster on adoption, Britain’s leading trade association said in a post. The report says the prize could be worth up to £150 billion (about $202 billion) in gross value added over the next decade if robots and smart machines are taken up more widely.

The document is, in effect, a plea to turn research strength into commercial scale. techUK says advances in artificial intelligence, sensing, computing power and advanced materials are making robots more capable, connected and autonomous, with applications already spreading across healthcare, manufacturing, agriculture and infrastructure.

Across rich economies, robotics is becoming less a question of novelty than of industrial policy. Countries that combine research, manufacturing capacity and permissive regulation are best placed to convert automation into higher productivity, better services and new business models; those that do not risk watching their inventions commercialised elsewhere.

“Something significant is happening in the world of robotics, and it is happening fast,” Rory Daniels, head of emerging technology and innovation at techUK, said in the post on the association’s website. “The UK must double down on its areas of leadership and leverage these to support innovators, accelerate deployment, and empower our most successful companies to scale and export”.

techUK’s central argument is that the chief obstacle is not invention but uptake. Many UK firms, especially small and medium-sized enterprises, have yet to deploy even off-the-shelf robotics tools, a gap the report says must close if productivity is to rise and the economic value of innovation is to be captured at home.

To make that case tangible, the report cites examples from across its membership. Siemens says AI-powered robotic picking software can identify and grasp unknown objects in milliseconds, while the Manufacturing Technology Centre says robotic systems for tunnel installation have delivered up to 40 percent productivity gains and 30 percent cost reductions, alongside safety improvements.

The report also points to Airbus Defence and Space’s work on satellite servicing, debris removal and autonomous assembly, and to Oxa, which has raised £103 million to scale autonomous vehicle technology across heavy industrial logistics, light logistics and asset monitoring.

techUK sets out nine recommendations, including treating robotics as a frontier technology, strengthening the Department for Science, Innovation and Technology’s robotics capacity, using public procurement to drive adoption, and making robotics an explicit priority in the government’s Scale-Up Support Service.

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Accenture, Vodafone Procure & Connect and SAP are testing whether humanoid robots can do more than theatrically mimic human labour on a trade-show floor. In a warehouse in Duisburg, Germany, the partners say they have shown that physical AI can help spot safety risks, inspection failures and idle space with real-time reporting into SAP’s warehouse system.

The pilot matters because warehouse automation is moving from fixed machinery to systems that can observe, decide and act in less structured environments. That is a broader shift in industrial technology: firms want robots that can work beside people, use enterprise data and justify themselves not just as labour substitutes, but as tools for safety, compliance and operational control.

Against that backdrop, the partners are presenting the work at Hannover Messe 2026, where industrial software, robotics and supply-chain automation increasingly overlap. The appeal is not just cost-cutting, but the prospect of a new operating model in which physical AI feeds live data back into business systems and, eventually, into new services.

“Trained in digital twins and powered by physical AI, humanoid robots can reduce worker injuries and other warehouse safety incidents and lower overtime costs and the dependency on temporary labor,” Christian Souche, Accenture’s advanced robotics lead, said in a press release.

The pilot used humanoid robots at Vodafone Procure & Connect’s warehouse to carry out visual inspections through SAP Extended Warehouse Management. According to the release, the robots detected misplaced or damaged products, checked pallet stacking and weight distribution, identified unused storage space and flagged hazards such as obstacles in aisles or misaligned pallets.

SAP handled the integration with the warehouse management system, while Accenture built the robot intelligence and operating framework. The companies did not present this as a finished commercial product, but as a proving ground for a future “humanoid workforce” model that could be scaled if the economics and reliability hold up.

The humanoid robots used in the pilot are powered by Accenture’s Robot Brain solution. They are trained in digital twins of warehouse environments, built on Accenture’s Physical AI Orchestrator, which uses NVIDIA Omniverse libraries, the Mega NVIDIA Omniverse Blueprint and the NVIDIA Metropolis libraries and Blueprint for video search and summarization for the deployment of visual AI agents.

Vodafone’s logistics chief, Reinhard Stefan Plaza Bartsch, said the pilot was meant to explore “how humanoid robotics can improve efficiency, safety and operational visibility” and to clarify how such capabilities might scale across the supply chain. That is a sober ambition, and a sensible one: warehouses are where the promises of embodied AI will be judged by productivity, not by spectacle.

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Accenture has invested, through its venture arm, in General Robotics, an AI-native company that says its software can let organisations deploy and adapt robots of any form for any task. The companies will work together to help manufacturers, logistics groups and other asset-heavy industries move towards autonomous operations powered by physical AI.

The bet is small in balance-sheet terms, but it sits squarely in a larger industrial shift. Across factories and warehouses, firms are trying to push robots beyond isolated demonstrations and into systems that can learn, coordinate and adapt as production changes, a harder problem than simply adding more machines to the floor. That is why the race is increasingly about software layers, simulation and orchestration, not only motors, sensors and arms.

General Robotics’ pitch is that it offers a general-purpose intelligence layer that can sit across different robots and help them work with less custom programming. In Accenture’s telling, the investment is meant to speed deployment and make robotics more practical at scale for clients that want more output without proportionately larger workforces.

“Robotics powered by physical AI tackle challenges our clients encounter, including labor shortages, diminished productivity in factories and warehouses, and the ongoing increase in both capital and operational expenditures,” Prasad Satyavolu, Accenture’s global lead for manufacturing and operations, said in a press release. He added that the partnership would focus on an “enterprise-grade robotics intelligence and orchestration layer” to help companies deploy systems “safely, efficiently, faster, and at scale”.

General Robotics, founded in 2023 and based in Redmond, Washington, says its GRID platform connects robots, agents and AI models through a single system. The company says the platform relies on modular AI capabilities, cloud orchestration and simulation rather than fixed programming. Accenture did not disclose financial terms.

Ashish Kapoor, General Robotics’ chief executive and co-founder, added: “We’re providing the intelligence grid that connects robots, agents and AI models through a single platform designed to speed deployment and adapt as AI advances and robotic tasks become more sophisticated”.

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Google DeepMind has unveiled Gemini Robotics-ER 1.6, a reasoning model meant to help robots understand the physical world with greater precision, from spatial judgment to task planning and success detection. The upgrade also improves multi-view understanding and adds instrument reading, a capability developed with Boston Dynamics, according to a blog post shared by DeepMind’s Laura Graesser and Peng Xu.

The model is meant to serve as a high-level brain for robots, able to call tools such as Google Search, vision-language-action models and third-party functions as part of its planning. DeepMind says it performs better than both Gemini Robotics-ER 1.5 and Gemini 3.0 Flash on tasks such as pointing, counting and telling whether a job has been completed.

The announcement matters beyond one lab’s technical progress. Robotics has long been constrained less by hardware than by the difficulty of making machines reason reliably in messy, changing environments; advances in embodied reasoning could therefore accelerate use cases in factories, warehouses, inspection and service robotics worldwide.

“We are also unlocking a new capability: instrument reading, enabling robots to read complex gauges and sight glasses — a use case we discovered through close collaboration with our partner, Boston Dynamics,” Graesser and Xu said in the post.

DeepMind frames success detection as central to autonomy, arguing that robots must know not just how to act, but whether an action has worked. It also says Gemini Robotics-ER 1.6 improves multi-view reasoning, allowing the system to relate multiple camera feeds even when scenes are dynamic or partly obscured.

The company is also pitching the model as its “safest robotics model to date,” claiming superior compliance with safety policies on adversarial spatial reasoning tasks. That line will matter to industrial users, who want robots that are not merely clever but predictable. DeepMind says it is inviting developers to submit labelled images showing failure modes, suggesting the work remains a collaboration rather than a finished product.

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Industrial automation is accelerating as economies worldwide prioritise the integration of factory robots to bolster productivity. According to the World Robotics 2025 report by the International Federation of Robotics (IFR), Western European countries reached a record density of 267 robots per 10,000 manufacturing employees in 2024. This figure places the region ahead of North America, which recorded 204 units, and Asia, which averaged 131 units.

The European Union maintains a robot density of 231 units, significantly above the global average of 132. Within the region, Germany remains the primary driver, ranking third globally with 449 units per 10,000 employees, following an average annual growth of 5 percent since 2019. Other European nations in the global top ten include Sweden, Denmark, and Slovenia.

The global surge in automation reflects a fundamental realignment of industrial strategy as nations grapple with tightening labor markets and the imperative for supply-chain resilience. In an era where demographic decline and rising costs threaten traditional manufacturing models, robot density has emerged as a critical indicator of an economy’s ability to maintain its industrial edge without relying on a growing pool of human labor.

“The robot density metric provides a uniform basis for comparison by relating the total number of robots used in a country to its economic size, as measured by its workforce,” Takayuki Ito, President of the International Federation of Robotics, said in a post.

The Republic of Korea maintains the world’s highest robot density with 1,220 robots per 10,000 employees, benefiting from its dominant electronics and automotive sectors. Singapore follows in second place with 818 units, while Japan ranks fourth with 446. The United States ranks eighth worldwide with 307 units, representing a 4 percent year-on-year increase.

However, the most significant shifts are occurring in China. Although its density ranks 22nd globally at 166 units, its operational stock of 2 million units is the largest in the world — approximately 4.5 times that of Japan. In 2024 alone, China accounted for 54 percent of all global robot installations, deploying 295,000 units as it seeks to automate its vast manufacturing base.

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Agile Robots, a German maker of versatile robotic hardware, has struck a research partnership with Google DeepMind to infuse its machines with advanced artificial intelligence. The collaboration marries DeepMind’s Gemini Robotics foundation models — capable of multimodal reasoning — with Agile’s actuators, sensors and full-stack platforms.

The aim is to create adaptable systems for manufacturing, from electronics assembly to heavy logistics, where robots must handle the unpredictable.

The two firms will exchange real-world data to refine their technologies: Agile’s deployments, numbering over 20,000 units across factories in China, Europe and America, will feed DeepMind’s models, while the AI sharpens Agile’s hardware for tasks demanding vision, touch and planning. Initial applications target precision sectors such as 3C electronics, new energy vehicles, data-centre maintenance and heavy industry. Agile’s existing wares, blending German engineering with mass scalability, now gain a shot at true autonomy.

This pact underscores a pivotal shift in robotics, where disembodied AI meets physical form amid a $200 billion industrial-robotics market strained by labour shortages and geopolitical supply risks. As America’s Figure AI and China’s state-backed humanoids vie for dominance, Europe’s entry via DeepMind, Alphabet’s moonshot arm, hints at a transatlantic counterweight, potentially accelerating the dawn of factories run by thinking machines rather than mere repeaters.

“The huge opportunity ahead lies in autonomous, intelligent production systems that can transform entire industries. Integrating Google DeepMind’s Gemini Robotics models into our robotic solutions positions us at the cutting edge of this rapidly growing market,” Zhaopeng Chen, founder and chief executive of Agile Robots, said in a press release.

Yet factories pose sterner tests than labs: dust, variability and safety rules have dashed prior AI dreams. Agile’s foothold and DeepMind’s prowess in complex reasoning offer grounds for cautious optimism. Carolina Parada, head of robotics at DeepMind, hails the tie-up as “a vital step towards realising embodied AI that learns and adapts like never before”. Success could redefine toil from Shenzhen to Stuttgart.

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Jeff Bezos, the founder of Amazon, is in early talks to raise about $100 billion for a new investment vehicle that would buy traditional manufacturing companies and overhaul them with artificial intelligence, according to a report in the Wall Street Journal, echoed by Reuters and other outlets.

The effort is described as a “manufacturing transformation vehicle” focused on industrial mainstays such as chipmaking, aerospace and defence, according to the news reports. Bezos has been courting some of the world’s biggest asset managers, as well as sovereign-wealth funds in the Middle East and Asia, during recent trips to the region.

The fund, which would rival SoftBank’s $100 billion Vision Fund in sheer scale, marks one of the most ambitious attempts yet to wire AI directly into the foundations of heavy industry rather than the consumer internet. If successful, it would accelerate a broader shift in which capital and algorithmic know-how migrate from software and advertising to factories, supply chains and the “real economy.”

It also underscores how a small set of tech billionaires now treat large buyout pools as strategic weapons in an emerging contest over who controls the data and industrial capacity on which future AI systems will feed.

At $100 billion, the proposed pool would give Bezos a war chest big enough to roll up mid-sized industrial firms that are struggling with labor costs, capital-intensive retooling and the investment required to digitise their plants. For governments anxious about productivity, reshoring and defence supply chains, such a vehicle could look like a timely private complement to industrial policy — or, to critics, the beginnings of an unaccountable industrial conglomerate built in AI’s image.

The various news reports call the venture a bid to “drive and speed up automation” inside acquired companies, using AI models being developed at Project Prometheus, Bezos’s industrial AI startup. Bezos serves as co-founder and co-CEO of Project Prometheus, which is separately seeking up to $6 billion in fresh capital.

“The goal is to transform legacy manufacturers into AI-first companies that can design, simulate and operate complex systems far more efficiently than today,” one investor presentation says, according to people familiar with the pitch, as reported by the WSJ.

Labor advocates warn that aggressive automation could hollow out middle-skill jobs, even as investors bet that AI-optimised plants will pay for themselves through higher throughput and fatter margins.

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BMW Group is bringing humanoid robots into series production in Germany for the first time, extending an ambitious push to fuse artificial intelligence with factory automation. The carmaker will run a pilot at its Leipzig plant using AEON, a humanoid robot developed by long-standing partner Hexagon, to support assembly of high‑voltage batteries and component manufacturing from summer 2026.

The move follows a 2025 trial in Spartanburg, South Carolina, where Figure AI’s Figure 02 robot handled precise, repetitive body‑shop tasks in BMW’s X3 production line. Over ten months, Figure 02 worked ten‑hour shifts, five days a week, moving more than 90,000 sheet‑metal components and supporting the production of over 30,000 vehicles, while integrating with BMW’s existing smart transport robots and robotics ecosystem.

Automakers and technology groups are racing to deploy humanoid robots as labour markets tighten, vehicle architectures grow more complex and traditional industrial automation reaches its limits. Proponents argue that mobile, human‑shaped machines, powered by increasingly capable AI systems, can slip into brownfield plants, take on ergonomically punishing or safety‑critical tasks and be reprogrammed as models and processes change, potentially reshaping factory work in the same way industrial robots did in the late 20th century.

“Digitalisation improves the competitiveness of our production – here in Europe and worldwide. The symbiosis of engineering expertise and artificial intelligence opens up entirely new possibilities in production,” said Milan Nedeljković, BMW’s production chief.

BMW describes the initiative as part of a broader “Physical AI” strategy that combines digital AI agents, a unified data platform and robots able to learn and operate autonomously on the shop floor. It has created a Center of Competence for Physical AI in Production in Munich to vet technology partners, run lab‑based evaluations with real use cases and then move promising systems into plant‑level pilots.

At Leipzig, AEON will be tested as a multifunctional platform whose human‑like form factor can carry different grippers and scanning tools and move dynamically on wheels. The emphasis, BMW says, is on complementing existing automation, taking over monotonous and physically demanding tasks, and improving working conditions rather than replacing staff.

“Our aim is to be a technology leader and to integrate new technologies into production at an early stage,” said Michael Nikolaides, who oversees BMW’s global production network and supply chain.

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Humanoid Robots Are Entering the Workforce

Long a staple of science fiction, humanoid robots are edging into factory floors and warehouses, but Forrester expects the transition to be cautious rather than explosive. A new report from the research firm, ‘The State of Humanoid Robots, 2026,’ argues that the machines are shifting from spectacle to tool, with early adopters chasing productivity gains while wrestling with cost and regulatory headaches. Other analysts have proposed a staged adoption model, with each phase contingent on clearing the economic hurdles of the last.

Early Deployments Are Delivering Measurable Results

Forrester’s latest automation survey finds that 69 percent of automation decision-makers are adopting or planning to adopt humanoid robots, with deployments already underway in manufacturing, logistics, healthcare, and customer service. Early adopters report 40 percent reductions in processing errors and 20 percent declines in labor costs where humanoids standardize repetitive, high-friction work, from BMW’s use of robots for ergonomically awkward assembly to KEENON Robotics cutting restaurant labor costs through automated food preparation and cleaning.

The push comes amid a broader race to automate labor-intensive tasks as companies confront aging workforces, skills shortages, and persistent margin pressure, though the debate on humanoid viability remains unsettled. Investors and boardrooms are betting that advances in generative and physical AI, coupled with cheaper cloud-based infrastructure, will allow humanoid machines to operate safely and reliably in spaces built for people, even as regulators and risk officers fret about safety, cybersecurity, and liability.

“Humanoid robots are no longer a futuristic fantasy, but a pragmatic tool for operational transformation,” says Charlie Dai, a vice president and principal analyst at Forrester. “Leaders must approach this technology with disciplined experimentation, viewing these robots as workforce multipliers that augment human capabilities rather than wholesale replacements.”

AI Advances Are Accelerating Humanoid Capabilities

The report points to advances in AI to explain the acceleration. NVIDIA’s Isaac GR00T-Dreams, for instance, is said to cut model-development time from three months to 36 hours by using synthetic motion generation and multimodal foundation models, while physical-AI techniques narrow the sim-to-real gap to improve balance and reliability in real-world settings.

Humanoids Are Augmenting Workers, Not Replacing Them

For now, Forrester stresses that humanoids are amplifying human workers rather than displacing them. In warehouses, AgiBot’s A2-W handles 30 percent of material transport with zero errors, and in Singapore’s Sengkang Community Hospital, a robot called Dexie supports multilingual dementia care so staff can focus on more complex, higher-empathy tasks.

What Is Holding Back Large-Scale Deployment

Yet the firm is skeptical that a flood of chrome-plated colleagues is imminent. High R&D costs, deployment complexity, and underdeveloped rules on safety, cybersecurity, and liability will keep most projects at pilot scale for the next two years, it says, with prudent companies advised to take a measured, human-centric approach rather than succumb to humanoid hype. We examine the pilot to production gap in detail, including the operational and economic conditions that determine when manufacturers move from testing to full deployment.

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Agility Robotics, maker of the bipedal robot Digit, has signed a commercial robots-as-a-service agreement with Toyota Motor Manufacturing Canada (TMMC), turning a recent pilot into a full deployment at the carmaker’s plants in Ontario.

Digit units will be introduced to lines involving manufacturing, supply-chain and logistics tasks, with the aim of easing physical strain on workers and streamlining shop-floor operations, the Oregon-based robotics company said in a press release.

The deal follows a trial at TMMC’s Cambridge facility and will see humanoid robots integrated into existing workflows rather than driving costly retooling of production lines. Agility says its Digit platform and Agility Arc cloud software can be slotted into current layouts to handle monotonous, process-automated jobs that are hard to staff and retain, from repetitive material handling to backroom logistics. The humanoid robot economics behind deployments like this one, including payback periods and cost projections, are explored in our manufacturing analysis.

Humanoid robots have become the latest frontier in factory automation as manufacturers confront ageing workforces, labour shortages and pressure to raise productivity without sacrificing safety. Agility already counts Amazon, GXO and Schaeffler among its customers, and is pitching Digit as a general-purpose, “human-centric” machine that can walk where people walk and adapt to changing workflows using artificial-intelligence tools.

For global automakers such as Toyota, whose Canadian arm runs its largest production operation outside Japan, humanoids offer a way to automate ergonomically risky or low-value tasks while preserving the company’s long-cultivated “Respect for People” ethos on the line.

“Toyota Motor Manufacturing Canada has long been a leader in automotive manufacturing innovation,” said Tim Hollander, TMMC’s president. “After evaluating a number of robots, we are excited to deploy Digit to improve the team member experience and further increase operational efficiency in our manufacturing facilities.”

Beyond the initial rollout, Agility and TMMC plan to assess further use cases where robots and AI could augment automotive production, particularly in extremely repetitive and physically taxing jobs. Agility’s chief executive, Peggy Johnson, said the company’s next generation of Digit is intended to be the first “cooperatively safe” humanoid designed to work alongside people, enabling customers to scale deployments beyond fenced-off automation cells.

Agility argues that, as its robots learn new tasks and adapt on the fly, they will become a flexible layer of automation that can be reallocated as product mixes and production schedules shift.

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Bedrock Robotics and Machina Labs said in separate announcements they have raised fresh investments to automate some of the most labour- and capital-intensive corners of the industrial economy, underscoring investors’ appetite for software-defined hardware in old-line sectors.

San Francisco-based autonomous construction company Bedrock Robotics has raised $270 million in a Series B round to advance software-defined machines for infrastructure projects. The investment, co-led by CapitalG and Valor Atreides AI Fund, brings the company’s total capital to more than $350 million and pushes its valuation past $1.75 billion. Backers include NVentures, Tishman Speyer, MIT and C4 Ventures.

Bedrock, which emerged from stealth in July 2025 with $80 million in seed and Series A capital, retrofits excavators, bulldozers and loaders so contractors can deploy supervised and, eventually, fully autonomous fleets on complex infrastructure projects.

The new capital will help shift customers from piloting individual autonomous machines to orchestrating connected fleets across large sites. A recent deployment on a 130-acre manufacturing site validated its mass excavation capabilities.

Across rich economies, ageing workforces, infrastructure pushes and subsidies for factories building chips, batteries and data centres are colliding with chronic shortages of skilled tradespeople and rigid 20th‑century manufacturing assets. Investors are betting that autonomy and AI-native production lines can unlock capacity without a commensurate rise in headcount, recasting earthmoving and metal-forming as software problems rather than purely mechanical ones.

“The construction sector is facing an overwhelming demand that it cannot fulfill,” said Boris Sofman, Bedrock’s boss, arguing that system-level autonomy will let contractors redeploy scarce operators to supervision and strategy while fleets of machines run longer with less idle time.

Machina Labs

In Los Angeles, Machina Labs has secured $124 million in Series C financing to deploy its first “Intelligent Factory” for metal manufacturing in defence and aerospace. Led by an unnamed lead investor, the round will fund a 200,000-square-foot facility housing up to 50 RoboCraftsman cells. The company plans to produce thousands of metal assemblies annually for customers in defence, advanced mobility and automotive sectors.

The Intelligent Factory uses AI-driven robotics to replace traditional tooling, enabling rapid reconfiguration for diverse parts. Machina aims to cut lead times and support high-mix, low-volume production amid supply-chain strains.

For defence ministries, aerospace primes and carmakers fretting over supply-chain fragility, such technologies promise shorter lead times, more configurable plants and a measure of industrial resilience.

“The world’s most advanced designs are being held back by 20th-century factories,” said Edward Mehr, the company’s chief executive. “This round allows us to scale manufacturing infrastructure that moves at the speed of software. We’re not just making parts, we’re reprogramming the factory itself to serve defense, aerospace, and automotive customers who can’t afford to wait.”

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AI is rapidly boosting robots’ capabilities, efficiency and adaptability on factory floors and in service settings, the International Federation of Robotics (IFR) argues in a new position paper.

IFR highlights how deep-learning computer vision now allows robots to see and interpret visual data for tasks such as object recognition, barcode reading, sorting and real-time production-line monitoring, while supervised learning powers defect detection, predictive maintenance, quality inspection and process optimisation. Natural-language processing lets collaborative and service robots understand and respond to spoken or written commands, and mobile robots blend data from cameras and LiDAR to perform simultaneous localisation and mapping for warehouse and shop-floor navigation.

AI for robotics promises to bring the productivity improvements seen from generative AI to the physical world, even as governments and boardrooms alike try to balance the gains from AI-enabled automation with unease over jobs, safety and the energy appetite of large models.

Robot installations are already taking over physically demanding and repetitive tasks, freeing workers from harsh conditions, even as AI adds demand for data scientists, machine-learning specialists and ethicists, and forces companies and employees to keep re-skilling to remain competitive. At the same time, the IFR notes, AI-enhanced efficiency and output may spur economic growth while intensifying pressure on businesses and workers to adapt to faster cycles of technological change.

“In future, AI in robotics will further influence how teams work, how decisions are made, and how performance is monitored,” the IFR notes, adding that improved workflows “may also raise concerns about employee surveillance or reduced autonomy.”

The federation notes that reinforcement learning, though still emerging in industrial settings, is gaining traction in motion and path planning, grasping and adaptive control, where robots learn by trial and error in dynamic environments. It singles out generative AI as the next step, predicting that models will generate code for entire robotic functions from natural-language instructions. The quality of AI-generated code should be rigorously tested IFR adds.

Developers and users, it cautions, must contend with data poisoning, biased or compromised training sets and the unpredictability of autonomous systems, since malfunctions in the physical world can have more severe consequences and human-robot collaboration must remain physically safe at all times. A growing focus on sustainability will push robotics towards efficiency and longer robot lifespans, the IFR says, even as the sector confronts the ecological cost and carbon footprint of training large AI models.

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Humanoid robots, long a staple of science fiction, are edging closer to commercial reality as companies begin structured trials in factories, warehouses and commercial buildings, according to new analysis from Bain & Company. The consultancy argues that the timing and scale of adoption will depend less on gee-whizz demonstrations and more on prosaic factors such as power systems, sensor density and measurable returns on investment.

Bain says the technology stack for humanoids is progressing unevenly: intelligence and perception are advancing quickly but are still short of enabling fully autonomous operation, while power systems and dense, reliable sensing remain stubborn bottlenecks. Large-scale penetration, it contends, will only come once those constraints ease, safety and privacy concerns are addressed, and early adopters can prove positive returns that justify broader rollout.

Behind the cautious timeline lies a bigger wager about the future of work and automation. Over the next decade, Bain expects humanoid robots to move from experimental deployments to large-scale use across three successive waves —industrial, commercial and then consumer — potentially reshaping labour-intensive sectors from automotive and mining to healthcare, hospitality and domestic services.

Their spread will test how quickly firms, regulators and workers are willing to trust machines that look and move like people, yet are designed to slot into tightly optimised workflows rather than to replace humans wholesale.

“Humanoid robots are on the way, but their success depends as much on economics and trust as on technology,” the report concludes.

In Bain’s schema, early industrial use cases span automotive production, mining and construction, where humanoids can step into dangerous, variable or ergonomically awkward tasks that traditional fixed or wheeled robots struggle to handle. Our analysis of industrial humanoid deployments at BMW and Toyota examines how this first wave is playing out on factory floors today.Commercial settings such as professional cleaning, healthcare and hospitality — tour-guide roles among them — are expected to follow as software improves and operators gain confidence.

Consumer adoption, in areas such as domestic cleaning and education, is likely to lag until costs fall and reliability is proven in harsher industrial environments. Bain foresees a hybrid future in which non-humanoid machines handle highly repetitive workflows, while humanoid robots act as “flexible generalists” alongside humans, who focus on strategic oversight such as planning, workflow orchestration and risk management.

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The world’s biggest technology companies have kicked off 2026 with announcements of ambitious plans to further develop AI for the physical world, and especially in the field of brains for robots and humanoids. Two more announcements that are noteworthy come from Microsoft, and AsiaInfo and ABB Robotics.

Microsoft has introduced Rho-alpha, a new robotics model that aims to bring the generative-AI revolution from text and images into the physical world. Built on the company’s Phi series of vision-language models, it is designed to translate natural-language instructions into control signals for dual-arm robots performing complex manipulation tasks.

Rho-alpha is described as a “vision-language-action-plus” system, adding tactile sensing to the usual camera and language inputs and with plans to incorporate force and other modalities. Microsoft is trialling the model on dual-arm and humanoid platforms and is inviting organisations to apply for an early-access programme, ahead of broader availability through its Foundry offering.

The launch underscores how big technology firms are racing to turn large AI models into “physical AI” platforms that can perceive, reason and act among humans, not just generate text or images. As labour markets tighten and factories, warehouses and hospitals hunt for productivity gains, adaptable robots that can learn from feedback and cope with messy real-world environments have become a strategic prize.

“Through these advancements, we aim to make physical systems more easily adaptable, viewing adaptability as a hallmark of intelligence,” Microsoft’s research team wrote. “We believe robots that can adapt more easily to dynamic situations and to human preferences will be more useful… and more trusted by the people who deploy and operate them.”

AsiaInfo, ABB Robotics team up

Meanwhile, AsiaInfo and ABB Robotics have launched a new “Embodied Intelligence Laboratory” in China, betting that “physical AI” will be the next frontier in industrial automation. The lab was formally inaugurated on Jan. 25 and is positioned as the practical outgrowth of the two companies’ existing strategic partnership.

Backed by Alibaba Cloud and Nvidia, the laboratory will blend AsiaInfo’s strengths in AI applications, 5G-A communications and cyber security with ABB’s know‑how in robot control. The partners plan to focus on industrial simulation and physical AI built on Alibaba Cloud’s vision‑language‑action large model and Nvidia’s simulation platform, targeting deployment in complex factory environments.

The tie‑up underlines how Chinese software, Western industrial automation and US chip design companies are converging around embodied intelligence as manufacturing digitises at speed. As factories push to cut labour costs and cope with ageing workforces, vendors from ABB to Alibaba and Nvidia are racing to turn generative and embodied AI into robots that can perceive, decide and act reliably on the shop floor.

“The Laboratory may serve as a key platform for their continuous enhancement in technological synergies and co-creation of application scenarios, with a view to advancing the implementation of the application scenarios of physical AI in manufacturing sector and setting an industry intelligence benchmark with global influence,” AsiaInfo’s board said.

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Humanoid, a London-based robotics startup, has run its first live factory trial with Siemens, deploying a wheeled humanoid robot to handle box-moving chores inside one of the German group’s electronics plants. Over a two-week proof of concept in Erlangen, the HMND 01 Alpha robot shuttled plastic totes from storage stacks to a conveyor, working alongside humans in regular production.

The exercise was designed to see whether a general-purpose humanoid can take on the unglamorous but necessary work of industrial logistics, rather than the carefully fenced-off duties of traditional industrial arms. In Siemens’ tote-to-conveyor “de-stacking” task, the robot repeatedly picked containers from a pile, trundled them across the shopfloor and deposited them at a handover point for human operators until the stack was empty.

Humanoid robots have become the latest obsession in factory automation, as manufacturers hunt for flexible machines that can slip into existing layouts and workflows rather than demand greenfield redesigns. From carmakers to e‑commerce giants, companies are testing legged and wheeled bipeds in warehouses and plants, hoping they can eventually tackle varied, dull and physically taxing jobs amid labour shortages and ageing workforces. Siemens’ trial suggests that, at least for tightly defined tasks, the technology is moving from lab demo to production line experiment.

“Our focus is on creating robots that deliver measurable value in real-world settings,” Artem Sokolov, the UK company’s founder and chief executive, said in a press release. “This joint PoC with Siemens showed clear potential for practical deployment of humanoid robots. We see them move steadily toward the real world, and partnerships like this one help accelerate that transition.”

The PoC unfolded in two phases, starting with in‑house development using a physical twin of the workcell to refine performance before shifting to the live factory. In Erlangen, HMND 01 ran autonomously for stretches of more than 30 minutes, handled two tote sizes and met targets including 60 tote moves per hour, uptime above eight hours and pick‑and‑place success rates exceeding 90 percent.

Both companies bill the trial as a beginning rather than a climax. They are weighing additional logistics use cases and a broader rollout across Siemens facilities, turning the Erlangen factory — described by the group as its “customer zero” — into a test bed for what may become a new class of industrial co‑workers.

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Arm Holdings, best known for low‑power smartphone cores, is reorganising itself around the robots of tomorrow. The British chip designer has carved out a new Physical AI division to sell semiconductors and software for machines that move through the world, from factory bots to autonomous cars, as it chases one of the most tantalising growth markets in technology.

The unit will sit alongside two existing lines — Cloud and AI, and Edge — and will fold in Arm’s automotive business, underscoring how closely carmakers’ needs now resemble those of robot-builders.

Executives disclosed the plan on the sidelines of CES in Las Vegas, where robotics and so‑called ‘physical AI’ systems are hogging the limelight. Drew Henry, who will lead the new division, argues that smarter machines could “fundamentally enhance labour” and eventually move the dial on global GDP, Reuters reports.

Arm plans to add specialised robotics staff, hoping that tighter alignment of automotive and robotics — both obsessed with safety, reliability and power budgets — will give its designs an edge over rivals.

The move reflects a broader turn in artificial intelligence from disembodied chatbots to systems that sense, decide and act in the physical world. At CES, Nvidia and others are promoting “physical AI” stacks and robot foundation models, betting that the next wave of demand for compute will come from warehouses, roads and homes rather than just cloud data centres.

As chipmakers chase this shift, they are jostling to supply the standard architectures for humanoids, industrial arms and autonomous vehicles, much as Arm’s blueprints already underpin most of the world’s smartphones.

For New York listed Arm, robots promise a way to diversify beyond handsets while preserving its reputation for frugal, deterministic compute. Chief executive Rene Haas has already pushed through price rises and floated plans to design more complete chips, and the new division gives investors a cleaner story about long-term growth in automotive and automation.

Yet competition will be fierce: Nvidia, Qualcomm and a swarm of startups also covet the sockets inside future robot fleets, where software ecosystems and safety certification may matter as much as clever silicon.

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Hyundai Motor Group used the glare of CES 2026 to recast itself not just as a carmaker dabbling in robots, but as an architect of a Physical AI economy built on humanoids, co-working machines and data-rich factories. Under the banner “Partnering Human Progress”, the South Korean group laid out a robotics strategy that stretches from industrial cobots on today’s shop floors to Atlas humanoids mass-produced by the end of the decade.

The initiative rests on three pillars: putting robots alongside workers in hazardous and repetitive tasks; wiring the group’s far-flung value chain into an end-to-end robotics platform; and enlisting leading AI labs to supply the brains for increasingly capable machines.

The company is also planning a Physical AI Application Centre and a dedicated robot manufacturing and foundry plant, signalling that robots are moving from side project to core industrial franchise.

The push comes as global manufacturers race to convert decades of automation into fleets of adaptive machines that can see, reason and learn from the physical world, rather than simply repeat pre-programmed motions. From smart factories in Asia to logistics hubs in America and Europe, a new contest is emerging over who can turn proprietary data, AI models and hardware scale into defensible positions in “physical AI” – the layer where software meets gears, actuators and balance.

For carmakers like Hyundai, the wager is that control of both the robots and the factories they inhabit will be as strategic in the 2030s as control of the combustion engine was in the 20th century.

“The convergence of robotics and AI represents more than a technological advancement. It is a transformative innovation that will make human life safer and more enriching,” said Zachary Jackowski, vice president and general manager of Atlas at Boston Dynamics, Hyundai’s robotics subsidiary. “By combining capabilities of Boston Dynamics and Google DeepMind through this strategic partnership, we are taking a significant step toward redefining the future paradigm of the industry.”

Hyundai has already put Boston Dynamics’ robots to work, turning the company’s once-viral machines into earnest factory hands. Spot, the four-legged inspection robot, now operates in more than 40 countries, collecting data and monitoring safety in industrial sites, while Stretch, a warehouse robot launched in 2023, has unloaded more than 20 million boxes under harsh conditions. Those deployments, Hyundai argues, are proof that its robots can graduate from spectacle to scaled infrastructure.

The more audacious bet lies with Atlas, the humanoid platform that Hyundai expects to become the largest segment of the Physical AI market. The group plans to integrate Atlas into plants such as Hyundai Motor Group Metaplant America in Savannah, Georgia, before rolling it across the network once safety and quality gains are proven.

From 2028 the humanoids are due to take on tasks like parts sequencing; by 2030 the remit extends to component assembly, repetitive motions and heavy lifting, with the long-term goal of saturating entire production sites.

To support that rollout, Hyundai is constructing what it calls a Group Value Network, effectively an end-to-end AI robotics value chain built on its automotive production infrastructure, safety know-how and affiliates’ technologies. Within this framework the company aims by 2028 to reach a scalable system capable of turning out around 30,000 robots a year, a volume it claims will put the production version of Atlas ahead of any other enterprise-grade humanoid. The same supply chain that underpins electric vehicles and batteries is meant to give the group a cost and integration edge in robots too.

The strategy also leans heavily on software, particularly AI models adapted from software-defined vehicles to robots and other physical AI products. By digitising data from manufacturing, logistics and sales, Hyundai wants a virtuous circle in which robots constantly refine their behaviour and, in turn, generate richer data for training. The Physical AI Application Centre is pitched as the hub for this loop, curating use cases and feeding improvements back into both product design and factory deployment.

Hyundai’s ambitions are reinforced by a high-profile alliance between Boston Dynamics and Google DeepMind to accelerate next-generation humanoid development. DeepMind’s Gemini Robotics foundation models, built on its large multimodal Gemini system, are designed to let robots perceive, reason, use tools and interact more naturally with humans, regardless of form factor. Together, the partners aim to develop AI controllers for complex robots performing high-impact tasks, while stressing that any scaling of robot adoption must be done safely and efficiently.

On the show floor in Las Vegas, Hyundai is keen to demonstrate that this is more than a slide-deck strategy. Its booth at the Las Vegas Convention Centre features live demonstrations of Spot, Stretch and Atlas alongside in-house creations such as the X-ble Shoulder, an automatic charging robot and MobED, a mobile eccentric droid that has already won a CES 2026 Best of Innovation award. Motional’s Ioniq 5 robotaxi and Hyundai WIA’s autonomous mobile, collaborative and parking robots round out a line-up meant to show robots embedded in both everyday life and industrial workflows.

The group’s messaging stresses “human-centred automation”, with people remaining in control even as robots take on more of the dirty, dull and dangerous work. In Hyundai’s telling, factory workers graduate from manual labour to training and supervising robotic colleagues, while the broader ecosystem shifts towards large-scale robot commercialisation across sectors beyond mobility. If the plan works, the company will not just sell cars and robotaxis, but lease out legions of embodied AI systems that move, lift and watch on behalf of clients worldwide.

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