China is targeting the next industrial revolution by doubling down on embodied AI and humanoid robotics, building directly on its vast manufacturing infrastructure.
By Ellen H. Rumley and Allison Okamura
Chinese robots have quickly entered global public consciousness in recent years, appearing as background robo-dancers in pop concerts and as humanoid marathon and sprint runners outpacing elite athletes (1). Domestically, robots are deployed as traffic guides and restaurant service staff, while swarms of drones entertain audiences in urban light shows or deliver takeout to high-rises (2, 3). These public debuts serve to both normalize automation in everyday civilian life (4) while projecting international soft power.
Behind the public spectacle lies a bold agenda for global economic dominance. In its geopolitical contest with the United States, Beijing sees robotics as the core engine to project power and dictate the future of global supply chains (5). This ambition is further accelerated by domestic drivers of an aging population and a shrinking industrial workforce, compounded by the legacy of the one-child policy and rapid urbanization. Alongside targeted talent acquisition initiatives like the K-Visa (6, 7), China aims to pioneer a new era of industrial leadership while mitigating a long-term structural gap through a state-orchestrated robotics surge.
The Chinese Communist Party leverages its central authority to align state enterprise, private capital, and supply chains towards common strategic objectives, carrying the momentum to implement sweeping technological changes. Since the 1950s, every five years the government has produced large-scale roadmaps detailing major objectives mandated across its various levels of government. Called the Five-Year Plans (FYPs) – these documents provide bold economic development initiatives that are credited for structural reform like the opening of China to foreign trade in the 1980s (8). The FYPs have been influential in strengthening the nation’s manufacturing base and is now playing a defining role in advancing Chinese robotics.
The 13th FYP (2016-2020) laid the critical foundation of China’s robotics ascent and operationalized Made in China 2025 (MIC2025), a campaign to transition China from a “low-cost global factory floor” into a sovereign high-tech manufacturing leader (9). While most leading industrial nations historically outsourced production, China, as a historical receiver of outsourced production, took advantage of their vast manufacturing infrastructure to invest early into physical foundries and integrated supply chains that many nations are now spending heavily on to re-shore within their own borders (10). Key goals of MIC2025 were to climb up the global value chain across strategic sectors like robotics, using massive government subsidies to offset production costs (11). While many international critics condemned these subsidies as market distortion and imposed sanctions as a response (12), paradoxically this intensified Beijing’s efforts towards a sovereign tech sector.
Foreign frictions did ultimately lead Beijing to retire the branding “Made in China 2025”, but its core robotics objectives were carried out through focused sectoral plans in the 14th FYP (2021-2025). The Ministry of Industry and Information Technology (MIIT) even introduced two robot-specific initiatives: the “14th Five Year Plan for the Robotics Industry” (2021), which granted state-support for R&Ds in an effort to replace foreign suppliers of critical robotic components with domestic supplies, as well as the “Robot + Application Action Plan” (2023), which stimulated robotics demand by mandating adoption of domestic robotics across ten key sectors including agriculture, logistics, and health. This dual coordination of supply and demand, along with the push for general industrial leadership, enabled several impressive milestones to be achieved by 2025: China increased domestic market shares in industrial robots from 30% to 57% within a decade (13, 14), and domestic suppliers for industrial robots became market leaders for the first time (15). They reached 295,000 units of industrial robot installations in 2025 alone – 54% of all global installations that year – and entered the top five global manufacturers rankings for industrial robotics. Regional robotics hubs also flourished, including AI R&D clusters in Shanghai, hardware manufacturing bases in the Yangtze River Delta (16) and a growing startup ecosystem in Shenzhen (17) – creating a full-stack environment capable of rapidly prototyping fully integrated robotic systems. Local sourcing of many basic hardware components was further made possible by the massive mining and refining infrastructure that China possesses – which processes over 70% of the global production of rare earths like neodymium, which are critical for hardware like electromagnetic motors (18).
The 13th and 14th FYPs propelled China into a position of industrial robotic leadership in production scale, installation quantity, and cost effectiveness. Despite their indisputable achievements, some critical milestones were not met. For one, domestic robotics supply chains did not approach the levels of self-sufficiency expected by 2025. While Chinese robotics command near totality of domestic market shares in lower-stakes industries like textiles, they captured closer to 30% of domestic market shares for higher stakes sectors like automotive industries (5), and even less so – at 15% – for high-end machine tools (19). This disparity stems from a historical deficit in high-precision engineering in China. Key components essential for durable industrial-grade robots, such as strain-wave reducers, precision gearboxes, and high-performance servo motors, remain reliant on imports from Japan and Germany (20), where precision manufacturing traditions are backed by decades of institutional standards and specialized labor (21, 22). The incoming 15th FYP does address this shortcoming, however, and prioritizes China’s advancement of precision hardware in the coming years (23).
Concurrently, Beijing fell short of its goal to achieve 70% self-sufficiency in semiconductor manufacturing, missing the mark by an estimated 40% (24). This discrepancy highlights a bottleneck for China’s robotics ambitions, which depend on advanced logic chips for model training. The sharp decline in Chinese purchases of U.S. AI chips by late 2025 reflects an accelerated push toward domestic chip sovereignty (25). Against the backdrop of export controls from the U.S. and rising tensions across the Taiwan Strait—the global leader of semiconductor manufacturing (26) – Beijing’s pursuit of computational autonomy serves to protect itself against supply chain chokepoints that could derail its incoming robotics plans.
The 15th Five Year Plan (2026-2030) marks a strategic shift from traditional advanced automation towards ‘embodied intelligence’ – defined as AI that physically interacts with real-world environments. Elevated to one of China’s top priority “future industries”, embodied AI is tasked with driving economic growth while modernizing national defense (27, 28). Elevating embodied AI from a niche subgoal to a central industry priority carries significant policy weight; central ministries, provincial governments, and state financial institutions are now mandated to tightly coordinate robotics development, reallocating substantial shares of state-backed venture capital and guidance funds to the sector (23).
Much like the 13th and 14th FYPs laid the groundwork for industrial automation, the 15th FYP launches a major campaign to accelerate physical AI through the “AI Plus Action Plan” (29). Under this initiative, Beijing is pushing for the widespread diffusion of low-cost, high-efficiency AI infrastructure across society – both domestically and across the Global South. AI integration is targeted across science, education, agriculture, green technology, defense, governance, and industrial development. Instead of pushing the frontiers of AI development, this policy prioritizes practical cross-sectoral applications that may already be feasible for domestic AI semiconductor chips – driving the iterative hardware advancements needed for Beijing’s long-term technology sovereignty (30). And as physical embodiment of AI, robots are expected to soon play a pivotal role in these directives (23). The real-world interaction of robots could provide vast physical dataset spillovers that further accelerate AI capabilities – a process supported by policies like “sandbox regulations” designating specific urban zones for real-world testing and training (31).
The 15th FYP specifically emphasizes the development of embodied AI in humanoid form factors. State subsidies are flooding the humanoid sector along large investments from tech giants like Huawei and Alibaba (32, 33), signaling a push from government and private entities alike towards the mass-deployment of humanoids in a manner reminiscent of the Chinese global domination of the electric vehicle (EV) market (34, 35). Owing also to the hardware surplus and supply chain synergies resulting from the EV push (36), China has already managed to scale its domestic humanoid sector to over 150 companies – resulting in 15,000 global installations, equivalent to 85% of global humanoid deployments in 2025 during the country’s first year of mass production (37, 38). Efforts for mass production were especially prominent at this year’s International Conference on Robotics and Automation (ICRA 2026), where Chinese humanoids and dexterous hand start-ups dominated the exhibit floor. Clearly, Beijing views humanoids as a leapfrog technology that could spearhead the next industrial revolution.
There are ongoing debates within the robotics community as to whether humanoids are the ideal form factor for general-purpose robots. On one hand, redundant joints for appearing human-like could introduce unnecessary mechanical inefficiencies and control complexity. On the other hand, humanoids could be, with enough data, an ideal form factor for interacting with everyday objects and urban spaces designed with people in mind.
Indeed, China’s focus on the humanoid form factor aligns closely with its data aggregation strategy: mapping directly to human workers allows a seamless transfer of human motion data into imitation learning models. In line, by July 2026, 40 humanoid training facilities had opened across major Chinese cities (39). In these hubs, humanoids from various domestic manufacturers are pooled to train basic skills – such as grasping and transport – in simulated real-world scenarios (40). Human operators wearing VR headsets and exoskeletons teleoperate the robots through task execution, capturing raw motion and spatial data. This supervised training is repeated hundreds of times daily, cleaned, labeled, and shared across manufacturers as foundational reference datasets. This shared public infrastructure enables scalable low-cost training useful for AI model developers – underpinning a broader national strategy towards leadership in generalizable physical AI (41).
Ultimately, Beijing is placing a large gamble on humanoids. While concentrating national resources has propelled China into the global market lead, it also exposes the sector to severe structural risks. Intense domestic competition can drive down hardware prices, squeeze profit margins and place firms at risk of overcapacity (42). Because policy-driven supply currently outpaces market demand, only a fraction of today’s 150+ companies are expected to survive the coming years (43, 44). While parallel R&D efforts can spur rapid innovation, the absence of a strong commercial demand risks investments from Beijing amounting to waste, which would further strain local government budgets.
This risk extends to investors as well: when government backing signals long-term viability before demand has caught up, the speculative value of humanoids can inflate beyond justification, creating conditions of volatility we see mostly recently playing out in the market. Unitree Robotics, the leading Chinese humanoid and quadruped manufacturer illustrates this pattern as it went public on the Shanghai Stock Exchange; shares surged as much as 629% intraday and has dropped by nearly 45% in the days following (45).
Ultimately, China’s 15th FYP represents a high-risk, high-reward strategy, leveraging state coordination and massive data scale to brute-force the commercial viability of humanoid robots. Whether Beijing succeeds in leapfrogging the world in embodied AI or stumbles under the weight of market overcapacity will carry high consequences for the international robotics industry and society at large.
Thanks for reading. Next up, we will be taking a road(map)-trip across the European Union and examine Brussels’ contrasting regulatory approaches to robotics.
Ellen H. Rumley – Policy Analyst
Allison Okamura – Vice President
IEEE RAS Science & Technology Watch Board
Artificial Intelligence has moved beyond experimentation. Today, organizations across virtually every industry have access to increasingly sophisticated AI tools capable of automating tasks, enhancing customer experiences, improving decision-making, and driving operational efficiencies at scale. Yet despite the rapid pace of […]
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By Ashwin R. Vasavada
Lights blink on as I enter the Rover Operations Center at NASA’s Jet Propulsion Laboratory in Pasadena, California, at 7:30 a.m. I’m the first to arrive, even though I already feel late.
Sometime during the night on Earth, the Curiosity rover finished its day exploring on Mars and beamed its latest collection of images and measurements to a Mars orbiter zooming by overhead. The orbiter relayed the data to Earth, where it now waits on servers here and at partner institutions around the globe.
Once our rover operations shift kicks off at 8:15 a.m., a few dozen engineers and scientists will have just three hours to check the health of the rover, analyze the new science data and agree on the next set of rover activities. Then we’ll spend another four hours turning those plans into rover commands for tomorrow, making sure they are safe and fit within the rover’s available time and energy. Not long after that, the Sun will rise on Mars, and Curiosity will look toward Earth, expecting its next instructions.
As Curiosity’s project scientist, it’s my job to ensure that what emerges from this rush is a set of measurements that advance the mission’s science objectives and keep it on track to achieve what our team promised NASA and, ultimately, the public. It’s a seemingly overwhelming task given everything that must happen in the next seven hours. But after repeating it over a thousand times since Curiosity landed in 2012, our team has gotten pretty good at it.
NASA Curiosity project scientist Ashwin Vasavada guides this tour of the rover’s view of the Martian surface.
NASA created Curiosity to search for evidence of ancient habitable environments, such as those with liquid water and the chemicals, nutrients and energy sources required for life. The investigation called for a long-lived, mobile spacecraft that would allow scientists on Earth to virtually explore a local area on Mars, select and acquire rock samples, and analyze them in onboard laboratories.
JPL responded with the car-size, drill-equipped Curiosity rover. NASA added a suite of scientific instruments and a science team from the United States and around the world.
NASA sent Curiosity to Mars’ Gale Crater to climb Aeolis Mons, a mountain whose 3 miles (5 kilometers) of sedimentary rock layers hold a record of environmental conditions from about 3.5 billion years ago. The evidence suggests that back then a thicker, ancient atmosphere sheltered flowing streams and sparkling lakes.
Curiosity has climbed through a vertical half-mile (1 kilometer) of rock layers so far, finding clay-rich, mudstone layers that give way at higher elevations to younger sandstones full of salty minerals. Our team determined that lakes persisted for millions of years before the climate became arid and sand dunes overtook the lakes, although groundwater occasionally breached the surface to produce streams that wove among the dunes.
Samples Curiosity drilled from the lake sediments contain small organic – meaning carbon-based – molecules, the raw materials for potential life. The association of wet environments, organic molecules and a mix of chemicals similar to those that microbes harness for energy on Earth allowed our team to conclude that conditions in Gale were once capable of supporting life. Determining conclusively whether life actually took hold will require bringing such rocks back to laboratories on Earth.
As my colleagues arrive in the building and online, I’m lost in the latest images. Curiosity is well into the higher and drier strata of Aeolis Mons, yet the rocks have salts, scours and other signs of ancient water. These images suggest that perhaps even Mars’ dry period provided habitats for life.
During our previous shift, I asked the engineers who plan the rover’s route to take it into an area that appears exceptionally smooth and flat on images taken from orbit. After years of rough offroad driving, they were excited to open up the throttle and let the rover drive 120 feet (37 meters) across this Martian “parking lot” – a pretty long distance for Curiosity.
Polygon fractures with honeycomblike textures, discovered by NASA’s Curiosity Mars rover. NASA/JPL-Caltech/MSSS
What I’m looking at now is taking my breath away. Instead of smooth slabs, every surface around the rover is textured by small polygons, each a few inches across, as if someone covered Mars in honeycomb wallpaper. Our geologists suspect that the polygons outline fractures that formed through drying, like cracks in mud do, or through thermal cycles, or compression.
My computer screen shows the area in front of Curiosity covered by dots, each one representing a candidate target for the rover to observe with its cameras, its laser spectrometer, or the sensors on its arm. There are way too many dots, which is not a bad problem to have. A science team member trained to moderate the day’s discussion begins to narrow the list. Distinguished faculty, postdocs and students from around the world take turns advocating for their target.
The debate centers on what set of images and chemical measurements will best help distinguish how the polygons formed. I’m the tiebreaker if our team can’t quickly reach consensus, but that’s rarely needed. It’s Friday, so the three days of activities we plan for the rover today will take place over the weekend, and the results will arrive for our next shift on Monday. Curiosity works weekends.
Later on, I walk into the adjacent room where a robotics engineer visualizes in 3D how Curiosity’s five-jointed arm will reach the winning rock targets. I sit down next to another engineer who is simulating the rover’s next drive. We agree that the terrain ahead is rough: no more parking lot, all curbs. We discuss how she might steer the rover around the rock obstacles to reach the next science waypoint.
By noon, the science team’s role in operations is done, but my colleagues linger online to discuss the latest data in more detail. Meanwhile, the rover and instrument operators begin converting the day’s scientific requests into hundreds of commands that must be sent to NASA’s Deep Space Network in a few hours. There’s still a lot to do, but the team is in the homestretch for the day.
On Mars, the horizon is starting to brighten. Before too long, Curiosity will be expecting to hear from us.
This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract (80NM0018D0004) with the National Aeronautics and Space Administration.
OpenClaw – a viral software program that has captured the world’s imagination regarding the power of AI agents – is out with a major upgrade – OpenClaw 2.0.
The multi-tasking AI app can be used by writers, for example, to complete a number of steps in a highly complex research, writing and editing project – all without human intervention.
The free tool is considered revolutionary by many AI insiders – but needs to be used carefully, lest the app starts making too many decisions on its own.
There’s also a new demo/tutorial out on OpenClaw 2.0.
OpenClaw 2.0 is open source software (free for download) and can be run on an everyday laptop.
In other news and analysis on AI writing:
*ChatGPT Gets More Competition From Meta: Facebook parent Meta has rolled out an array of new paid AI tools designed to compete with ChatGPT, Gemini, Claude and similar.
Dubbed “Meta One,” the AI tools are designed to work with one or more Meta platforms – such as Facebook, WhatsApp and Instagram, starting at $2.99/month and scaling to $499/month.
Moreover, a special set of tools specifically designed for business-oriented creators is also available under the Meta One umbrella.
*Special-Use AI Models Take-Off: AI models trained for use by specific industries are growing in popularity, as increasing numbers of businesses gravitate to AI that ‘gets them.’
Thomson Reuters, for example, has developed its own AI model tweaked for law and tax professionals.
And Salesforce has released its own model Koa, designed for pros working in sales, marketing and customer support.
Meanwhile, Anthropic just rolled-out Claude for Financial Advisors.
*Script-to-Video-App – Complete With AI Spokesperson — Scores High on G2: AI video-maker Synthesia is a hit with G2 – a software rating site – where 2,000+ users have given the app 4.7 out of 5 stars.
Businesses use the app by selecting a photo-realistic AI spokesperson, adding a video script and watching Synthesia deliver the finished video in 20-minutes-or-less.
You can also go back and play with Synthesia’s editing tools if you want a more customized result.
*New Pew Study: Globally, Many People Equate AI With Job Loss: As the shadow of AI looms ever-larger across the globe, increasing numbers of people fear the spread of AI will lead to fewer jobs.
Observes lead writer Laura Silver: “In 34 of 37 countries Pew Research Center surveyed earlier this year, people tend to believe AI will lead to fewer jobs rather than more jobs.”
A real eye-opener from the study: An easy-to-read chart showing AI fear – expressed as a percentage – in each of the 37 countries surveyed.
*AI Marketing Conference Slated for Oct. 13-15: Sign-up for MAICON 2026 – an AI conference designed for marketers and business leaders – is open.
The meeting focuses on how enterprise and mid-market marketing teams are implementing AI across strategy, operations, content, analytics and customer experience.
Scheduled speakers include Andrew Yang, AI expert and former presidential candidate and Karen Hao, author, “Empire of AI.”
*HubSpot Drops New Guide on AI in Marketing: A new primer from HubSpot – “The Future of AI in Marketing” – is free for download for any marketer looking to make a quick study on AI and marketing.
Hubspot is known for putting out quality, extremely useful guides that are devoid of fluff.
*AI Chip Juggernaut Nvidia: Leave AI Safety to Us: Pushing back against growing calls for strict government regulation on the development of AI, Nvidia CEO Jensen Huang insists the AI industry can police itself.
Observes writer Julie Bort: “Huang sees little need for new laws at all. The free market, he argues, will be enough to pressure companies not to release unsafe products.”
*Microsoft AI Promises Self-Regulation: While AI titans like Google and OpenAI are begging U.S. legislators to regulate them, Microsoft is already promising to self-regulate.
A major player in AI, the software giant has published a draft code of conduct, featuring its own, self-imposed guardrails that will be used for the development of Microsoft AI.
Observes writer Jeffrey Dastin: “The code of conduct represents a constitution of sorts for future models made by the company.”
*China’s Response to AI Regulation: No Thanks: AI regulation advocates have received a ‘hard pass’ from China on the idea of imposing strict regulations to ensure the safe development of AI – at least the kind of regulation proposed by OpenAI, Anthropic and SpaceXAI.
For many, a China embrace of global AI regs is the only way such regulation will work.
Otherwise, a regulated U.S. and its allies would essentially be handing unregulated China the keys to the AI kingdom.
Observes an editorial in the “China Daily:” “The proposed coordination among the three companies (OpenAI, Antrhopic and SpaceXAI) sounds rather like a club whose membership rules have been drafted before the guest list is announced.
“Tellingly, David Sacks — who co-chairs the U.S. President’s Council of Advisors on Science and Technology — warned the three U.S. companies to stop pretending antitrust law has to be suspended so you can form a cartel.”
Share a Link: Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.
–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.
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Your AI factory can be fully operational while the business processes it was funded to improve remain unchanged. The infrastructure is in place, and your models are already running inference at volume. Yet when leadership asks which operating costs have fallen or which decisions are happening faster, the answer is “not yet.” And the cost of the infrastructure producing that answer is accumulating every quarter.
You made the case for this investment, and you stand by it. The board isn’t asking whether the investment made sense. It’s asking when it will produce the returns used to justify it. That question now requires a delivery date, not a status update.
You invested because your operating teams face problems that existing systems can’t solve. Decisions take too long. Assets underperform, and preventable disruptions remain expensive. Those problems still exist, and the reasoning behind the investment remains sound.
You’re also far from alone in struggling to turn AI investment into measurable results. BCG’s September 2025 report, The Widening AI Value Gap, found that 60% of companies reported minimal revenue and cost gains from AI. Substantial spending has not consistently translated into lower costs or new revenue.
The factory is producing. The question is what happens to that output once it leaves the model, and that is where most organizations are stuck.
The operationalization gap is the distance between the output your AI factory produces and an agent that can act within a live business process. Closing it requires a platform layer that builds, orchestrates, and governs agents connected to the people and systems responsible for acting on their output. That layer should handle three things the AI factory alone cannot:
For organizations where sovereign AI isn’t a preference but a condition of operating, particularly energy, public sector, and industrial, that governance layer must also remain inside the same perimeter as the infrastructure itself.
Consider a manufacturer whose factory already runs a model that flags maintenance issues before a production line goes down. It reliably identifies warning signs in equipment data, and those results support the case for intervening earlier to reduce unplanned outages.
Without the platform layer, however, the warning never reaches the technician responsible for the equipment. The team has no agreed criteria for when to inspect the equipment or escalate the warning, no fallback procedure when the sensor feed drops, and no record connecting the model’s recommendation to the action taken. The model may continue producing accurate results without changing how the business operates.
Getting the maintenance agent into daily use requires decisions from several teams. Infrastructure owns the hardware, while the AI and data team owns the model. Operations runs the production line and remains accountable for any action taken there. Security and legal also need to approve how the agent will operate.
Each team can complete its assigned work while the release remains stuck between them. Infrastructure is available, and the model is producing reliable output. Operations, however, is still waiting for something it can safely use. Without shared requirements and a release date, leadership has no reliable way to determine when the agent will enter the workflow.
Assigning someone to coordinate the effort won’t be enough if that person lacks authority. Whoever owns the deployment must be able to secure commitments across teams, resolve access questions, and move approvals forward. Otherwise, an open dependency can sit for months without counting as anyone’s missed deadline.
Every quarter of delay puts more pressure on the original business case. Whoever authorized the investment approved an expected return, with an assumption about how long that return would take to materialize. Costs continue accumulating while the expected benefits remain unrealized.
For the manufacturer, another quarter means continued exposure to the outages the agent was built to prevent. Savings lost during that period cannot be recovered simply by releasing the agent later. Meeting the original financial target will require greater returns in less time. If the timeline moves, the business case has to move with it.
When leadership asks when the agent will enter daily use, “it takes time” is no longer a sufficient answer. The delivery team should be able to identify the remaining work, name who is responsible for it, and give leadership a release date.
For a well-scoped use case, roughly one quarter should be enough to move from a model producing accurate output to an agent operating within the business workflow, provided the necessary access and decision-makers are available. That period allows time to connect the agent to live systems, test its behavior, and obtain approval for daily use.
Introducing a maintenance agent on one production line is a small, well-defined job. The team should be able to describe what remains before release and how long each dependency will take to resolve.
After more than a quarter without a clear release date, leadership should require a review of the outstanding requirements and a dated plan for completing them. Every dependency needs an owner who has the authority to act. If the team needs more time, it should explain what will be finished during the extension and how that work changes the release date.
For the manufacturer, progress becomes tangible when technicians begin receiving warnings through their normal workflow and acting on them under agreed procedures. Someone must then track whether those interventions are reducing downtime and identify where the agent’s performance or workflow needs to improve.
That first deployment creates more than a result for one production line. It establishes a working path from the factory into the business that future agents can inherit. Each new use case builds on decisions the company has already made instead of solving the same deployment problems again.
For a practical look at what it takes to move agents into production and operate them at scale, read the Operating agentic AI at scale ebook.
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