US Senators have joined bipartisan efforts by lawmakers in the Congress to create a national commission that will urgently look for ways to regain America’s leadership in robotics even as China threatens to run away with the prize.

Senator Dave McCormick, the Republican from Pennsylvania, is driving the measure in the Senate. On June 4 he introduced S. 4686, the Senate version of the National Commission on Robotics Act, alongside Democrats John Hickenlooper and Martin Heinrich and Republican Todd Young.

This comes on the heels of a plan introduced by lawmakers in the US Congress to do for robotics what the house once did for semiconductors and space: define the race before it is lost. A new bipartisan bill would create a temporary national commission to examine America’s robotics competitiveness, supply chains, workforce needs and security risks, but its real value may lie in forcing Washington to confront how badly the country has drifted behind.

The bill, the National Commission on Robotics Act, was introduced on February 3 by Jay Obernolte, Jennifer McClellan and Bob Latta. It would set up an 18-member commission under the Commerce Department, with experts drawn from industry, academia and government, and ask for an interim report within a year and a final report within two.

The wider context is global and unforgiving. China installed nearly nine times more industrial robots than the United States in 2024, according to the International Federation of Robotics, while the EU is building a tougher regulatory perimeter around AI and robotics through its AI Act, which is becoming a reference point for safety, transparency and oversight.

“Is it too late for America to regain its leadership in robotics? My answer is no, but we need to act now,” Jeff Burnstein, president of the Association for Advancing Automation, told lawmakers at a House hearing this week. That urgency is not mere industrial lobbying; it reflects a broader fear that the next wave of automation, especially “physical AI”, will be set by those who combine capital, standards and scale fastest.

McCormick, a former hedge-fund manager with deep ties to Pittsburgh’s robotics scene, framed the commission as essential to preserving America’s technological edge over adversaries and building the workforce needed for a technology-driven future.

He highlighted Pittsburgh’s “Robotics Row” — home to companies like Gecko Robotics and Agility Robotics — as proof that American robotics leadership creates high-paying jobs and strengthens domestic supply chains, yet warned that dependence on foreign-manufactured robotics raises urgent questions about supply-chain security, economic competitiveness and national defence.

The commission itself is modest, almost deliberately so. It would not write a robotics code or create a new regulator; it would gather facts, identify gaps and advise Congress on policy. Yet in Washington, commissions often serve as political recognition that a sector has moved from novelty to strategy. The bill’s scope is telling: it covers competitiveness, deployment in industrial and commercial sectors, workforce incentives, foreign policies and supply-chain dependencies.

That breadth matters because robotics is no longer a niche manufacturing story. It is becoming a platform technology for factories, logistics, elder care and defence, which means the policy question is shifting from whether robots should be encouraged to how their spread can be made more resilient, more productive and less dependent on foreign suppliers. For Congress, the danger is not overregulation but delay: in robotics, as in other strategic technologies, indecision is a form of industrial policy too.

Subscribe now

NEURA Robotics has raised up to $1.4 billion in a Series C round, a sum that underscores how quickly “physical AI” has become investable shorthand for the next phase of robotics. The German company says the capital will help it scale its cognitive robots, expand production and push its platform deeper into industry.

The investor list is broad, spanning Tether, Qualcomm, Amazon, Nvidia, Bosch, Schaeffler, the European Investment Bank, imec.xpand, Lingotto Horizon and InterAlpen Partners. NEURA frames the round as a backing for a full-stack approach in which hardware, software and data are developed together, rather than assembled piecemeal.

The wider significance is clear: as AI investment shifts from chatbots and cloud software to machines that act in the physical world, robotics firms are being valued less like niche manufacturers and more like strategic infrastructure. That is why NEURA’s raise matters beyond Germany. It sits at the intersection of industrial automation, semiconductor ambition, logistics demand and a broader race to commercialise robots that can learn, adapt and be deployed at scale.

“The future of AI will not only live on screens,” said David Reger, Founder and CEO of NEURA Robotics. “It will move, interact, learn and work beside us in the real world. We believe Physical AI and cognitive robotics will become one of the largest technology shifts of the coming decades, transforming industries ranging from manufacturing and logistics to healthcare, services and household robotics.”

NEURA says it already has an order backlog and strategic deployment pipeline above $1 billion, and it aims to use the fresh money to lift production capacity sharply, with a longer-term target of shipping millions of robots by 2030. Bloomberg has reported that the round could value the company at about $7 billion, though NEURA itself has not publicly confirmed that figure.

The company’s pitch is straightforward: if AI is to move from screens into factories, warehouses and homes, the winners will need more than good models. They will need robots that can be manufactured, trained and sold at industrial scale.

Subscribe now

Standard Bots has raised $200 million in Series C funding at a $1 billion valuation, the latest sign that US investors still see room for a domestic robotics maker to scale in a market long dominated by Asian rivals. The Long Island company said the money will support production of its AI-native industrial robots and expand its manufacturing footprint in New York.

The round was led by RoboStrategy, with General Catalyst among the existing investors participating. Standard Bots described itself as America’s largest manufacturer of AI-native, industrial robotics, a claim that speaks less to present scale than to the ambition of building a vertically integrated robot maker inside the United States.

The broader contest is global. As manufacturers face labour shortages, rising wage bills and a search for more resilient supply chains, industrial robots are moving from specialist tools to strategic infrastructure, and countries are treating their manufacturing sector as a matter of industrial policy as much as engineering. In that race, the ability to build, not merely sell, robots on home soil has become a selling point for investors and policymakers alike.

“Robots are critical to the future of US manufacturing,” the company said in the release, framing the investment as part of a wider effort to keep advanced automation closer to American factories and customers. Standard Bots also said the funding will help it expand its Glen Cove facility to 70,000 square feet, a sizeable step for the company as it steps up efforts toward durable industrial scale.

The company’s pitch is that a simpler, AI-native stack can lower the bar for factories that have not traditionally deployed robots at all. It said it aims to capture 10 percent of new US industrial robot deployments by next year, a bold target that will test both demand and execution.

"AI-native robots are the essential power tool of the 21st century – the tool that will grow American manufacturing and help every worker to be a force at work," Evan Beard, co-founder, CEO, and Chief Engineer of Standard Bots, said in a press release.

"AI will allow industrial robots to do 100x more tasks with full autonomy. You just show your robot how it's done, and it learns through demonstration. The quickest way to get to full autonomy is through deployments, collecting real-world data, and iterating as fast as possible," Beard adds.

The company notes in its press release: the biggest opportunity in automation is the massive amount of essential work that robots still can't do. Its robots are designed to be taught through demonstration and observation rather than traditional coding, making advanced automation more accessible across a broader range of industrial tasks and environments, including customers like Sunoco, Lockheed Martin, Amazon, NASA, the U.S. Army, and hundreds of SMB manufacturers.

"Across our portfolio, we're seeing a clear shift from experimental robotics to systems that can deliver immediate, real-world value," Andrew Kang, CEO at RoboStrategy (Nasdaq: BOT), an actively managed closed-end fund focused on robotics, said in the release.

"Standard Bots stands out because they've solved one of the hardest problems in industrial automation: making robots that are not only powerful, but actually usable on the factory floor without specialised programming. Their approach to physical AI — teaching robots through demonstration — dramatically expands the range of tasks that can be automated. Combined with their commitment to building and scaling manufacturing in the U.S., we believe Standard Bots is uniquely positioned to define the next generation of industrial robotics," Kang adds.

Link to Standard Bot’s Post on LinkedIn

Subscribe now

The future of industrial automation surely involves the fairly straightforward idea of machines that see understand, and act. Yet the path from rudimentary obstacle detection to genuine spatial awareness has proven far more complex than early predictions suggested.

Today, as factories and warehouses worldwide face mounting labour shortages and efficiency demands – simultaneously attempting to move from single-purpose assemblies to flexible operations – the critical enabler of practical autonomy is not just more robots. It is robots that perceive their world accurately, navigate reliably, and operate safely alongside human workers.

This transformation hinges on advances in 3D sensor technology and its integration into unified perception systems. The stakes are substantial. A single collision in a warehouse can cascade into lost productivity, equipment damage, and even injury to humans. If we are to go from limited automation that won’t scale and may even prove a liability to automation that truly scales, we need to go from robots that only execute pre-programmed paths and those that adapt to unforeseen obstacles.

The perception problem

Consider what autonomous industrial robots must accomplish. They must detect people and objects in cluttered environments. They must function in harsh conditions — rain, dust, varying light, oil spills on factory floors. They must operate continuously, safely, and with minimal human intervention. Traditional 2D vision systems struggle with these demands. They fail in low light, lose efficacy with reflective surfaces, and require extensive computational overhead to infer depth. Automated guided vehicles (AGVs) confined to magnetic strips or reflective markers represent an earlier era of inflexible automation.

The advent of 3D sensing technologies — LiDAR, structured light, and time-of-flight cameras—has fundamentally altered what is technically feasible. LiDAR sensors generate point clouds with centimetre-scale accuracy across wide fields of view, even in complete darkness. Time-of-flight systems operate with lower latency and compact form factors, well suited to mobile platforms. Structured light offers high resolution in controlled environments. Yet deploying any single sensor modality across diverse industrial settings remains insufficient.

This reality has spawned a new discipline: sensor fusion. Rather than relying on one sensor, advanced autonomous systems now integrate data from multiple sensor types—cameras, LiDAR, ultrasonic sensors, inertial measurement units—to construct a more complete environmental model. When a camera struggles with glare, radar compensates.

When LiDAR performance degrades in rain, thermal imagery provides alternatives. The redundancy not only improves accuracy but serves a critical safety function: no single sensor failure can cascade into a collision.

The navigation challenge

The question of how a robot knows where it is, where it is going, and how to get there remains fundamentally difficult. Simultaneous localisation and mapping (SLAM) — the process of building environmental maps while simultaneously tracking position within them — depends entirely on the quality of sensor data flowing into the algorithm. A robot equipped with only low-resolution depth sensors cannot map fine details or detect small obstacles. One relying solely on visual odometry may fail when texture-less surfaces or rapid movement occur.

Advanced industrial robots now employ multi-modal SLAM systems that leverage complementary sensor strengths. Real-time processing of this fused data occurs at the edge — directly on the robot itself —rather than relying on cloud connectivity. This matters practically: a warehouse robot cannot pause for latency when navigating toward a moving pallet.

Edge computing, accelerated by various emerging computing platforms, enables perception pipelines running at 60 frames per second or faster with minimal computational overhead. This brings us a step closer to robots that adapt continuously to their surroundings, updating their maps and trajectories dozens of times per second.

Safety as an operational requirement

Safety in human-robot collaboration cannot be an afterthought or regulatory compliance exercise. It must be embedded into perception architecture from the outset. A robot that detects a person only after a collision has already occurred is a failed system. Modern implementations achieve this through multiple overlapping safety layers.

3D ultrasonic sensors like ADAR create virtual safety shields, detecting humans through walls or obstructions where conventional LiDAR might miss them. Computer vision systems identify unsafe postures or unauthorized zone entries in real time. Redundant sensors ensure that degradation of any single modality does not eliminate the safety function.

The operational payoff is significant. Warehouses deploying AI-powered safety systems report significant reductions in workplace incidents. Robots equipped with robust 3D perception operate effectively across far broader environmental conditions than their predecessors. A robot that handles rain, dust, reflective surfaces, and moving obstacles becomes deployable across real factories, not just laboratory environments.

Implications for industrial leadership

For companies building autonomous systems at scale, the technical architecture matters as much as the hardware choices. A full-stack approach — where navigation algorithms, mechanical design, sensor fusion, and software are developed cohesively rather than as modular components — enables the kind of environmental adaptation that industrial customers demand.

Such integration requires deep expertise across multiple engineering disciplines simultaneously: computer vision, control systems, mechanical engineering, and artificial intelligence.

India’s emergence as a serious contender in industrial robotics reflects this shift. Companies developing autonomous systems domestically benefit from proximity to manufacturing ecosystems, immediate access to complex real-world deployment environments, and engineering talent trained across multiple disciplines. When such companies achieve scale — deploying hundreds of robots across diverse factory and warehouse environments — they accumulate real-world data that refines perception and navigation algorithms in ways laboratory testing cannot. A robot that has executed a million autonomous missions in varied conditions learns patterns that static datasets cannot capture.

The road ahead

As 3D sensing technology matures, costs decline, and edge computing becomes ubiquitous, the competitive advantage shifts toward companies that integrate these capabilities into robust systems capable of scaling across geographies and applications. The future belongs not to the most sophisticated individual sensor, but to systems that fuse multiple modalities intelligently, process data at the edge with minimal latency, and continuously learn from operational experience.

For engineers, developers, and decision-makers seeking to implement safer, more efficient autonomous solutions, the lesson is straightforward: treat perception as infrastructure. Invest in multi-modal sensor fusion, prioritise edge processing, embed safety throughout the architecture, and demand evidence from real-world deployments. Autonomous robots are not coming to industrial facilities. They are already here. The question is whether your perception system is ready to guide them safely through your factory floor.

Subscribe now

For years, the industrial robotics industry has been obsessed with the wrong metrics. Factory executives have meticulously tracked robot uptime, battery cycles, and path efficiency. While these are important for maintenance, they ignore the fundamental reason why any manufacturer invests in automation: the flow of material.

To a plant manager at a global leader like Samsung or Yokohama, the robot is merely a means to an end. What truly matters is the Work-in-Progress (WIP) inventory it carries and how that material moves through the digital assembly line.

Today, the industry is on the cusp of a massive shift — from robot management to Material Orchestration. This shift is not just a change in terminology; it is a strategic response to a decade of unprecedented operational complexity.

The traditional focus on simple robot performance worked when factories were simpler. However, over the last 15 years, product SKUs have multiplied manifold. This explosion of variety has turned the factory floor into something of a black hole. While ERP and MES systems are excellent at tracking material when it enters or leaves a facility, they often lose sight of it the moment it hits the floor as WIP.

This has created a massive latent demand for a system that doesn’t just move robots, but manages the actual flow of value. Manufacturers are no longer looking for a hardware vendor; they are looking for a partner who can solve the WIP visibility crisis. This is where the concept of Material Orchestration becomes a competitive necessity.

The companies at the forefront of this evolution, are the ones moving beyond the ‘I make robots’ mantra to becoming leaders in agentic Material Orchestration. The core of this thesis is that true industrial value lies in the integration of physical agents (the robots) and software agents (the intelligence) working in a unified system.

There are also some siloed approaches in the market: some have focused on high-volume, cheap hardware, while others are working primarily on software-only solutions. The software moat is eroding quickly. In an era where AI is becoming ubiquitous, software alone is no longer a sustainable advantage. The real leadership edge belongs to those who can master the IT/OT convergence, merging data-processing power with physical control capabilities to orchestrate material autonomously.

The shift to Material Orchestration also redefines the role of the human worker. Today, the industrial worker is already no longer just a manual task-performer but changing into a strategic orchestrator. The material orchestration specialist’s system will support this by acting as a supervisor’s sidekick, if you will – an intelligent and capable one.

Instead of requiring a human supervisor to manually coordinate between ERP, MES, and a fleet of robots, the Material Orchestration System does the heavy lifting. It connects disparate data sources to track WIP in real-time, allowing the human supervisor to focus on high-level decision-making and strategic goals. This approach has already shown measurable impact in early industrial AI adopters, for example with significant reduction in query time for materials data.

The future of a global manufacturer’s factory depends on its ability to handle dynamic, high-complexity environments without requiring constant infrastructure changes like QR codes or lidar reflectors. A vision-based, agentic approach allows robots to understand the context of the material they are moving. For instance, if a robot perceives it is carrying fragile components, it can autonomously adjust its speed and path — true material-centric intelligence.

As we move toward a future of software-defined manufacturing, the companies that will win are those that stop talking about robots and start talking about outcomes. Early movers are already delivering these outcomes for global giants, moving tens of thousands of tons of material through their orchestration systems.

The era of tracking robots as isolated machines is over. The Material Orchestration story is the new standard for the autonomous factory. By focusing on the material — the actual lifeblood of production — this next generation of industrial robotics companies is not just helping factories run; it is helping them think.

For manufacturers facing skilled labor shortages and spiraling SKU counts, this shift represents the path to a perpetually adaptive enterprise.

Subscribe now

Humanoid, a UK robotics start-up, has turned a successful warehouse trial into a manufacturing deal with Bosch, a step that brings the company closer to moving beyond the demo phase and into industrial deployment. The agreement marks the transition to scaled production of Humanoid’s HMND 01 robot for the European market.

The proof of concept was completed in March in Bosch’s logistics environment in Bühl, Germany, where the robot transferred boxes from a conveyor to a trolley under autonomous control. Humanoid said the system handled five box sizes across different footprints, heights and weights, and that its KinetIQ AI framework orchestrated the workflow.

The broader significance lies in a familiar pattern in robotics: the real challenge is not proving that a machine can perform a task once, but making it reliable, manufacturable and economical enough to be deployed at scale. Industrial humanoids have attracted intense interest from automakers, logistics groups and suppliers because they promise a flexible answer to labour shortages and repetitive handling work, yet many remain stuck between prototype and production. Humanoid’s deal with Bosch is notable because it ties a technical validation exercise to the discipline of contract manufacturing, supply-chain planning and cost control.

“For Humanoid, this agreement is a critical step in our roadmap, bridging the gap between POC validation and large-scale deployment,” said Artem Sokolov, the company’s founder and chief executive. “Our goal has always been to shorten the path between innovation and real-world integration, and this agreement reflects that approach”.

Under the agreement, Bosch will serve as Humanoid’s contract manufacturing partner and provide Design-for-Excellence support across hardware design, production, supply chain and cost optimisation. The companies will also explore integrating Bosch components, including actuators, drives and sensors, into future versions of HMND 01. Humanoid said the arrangement is intended to speed adoption in logistics and manufacturing, though the road from promising pilot to durable factory tool is still long.

Subscribe now

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.

Subscribe now

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.

Subscribe now

Despite rapid advances in robotics, AI, and analytics, a majority of factories are only semi-automated at best. Adoption barriers often stem not from scepticism about technology itself, but from grounded business realities. Manufacturers remain cautious about automation largely because the business case is more complex than the technology pitch.

For many, automation is a strategic decision that must align with existing capital cycles, workforce readiness, and operational risk tolerances. The following five factors explain why even efficiency-driven manufacturers continue to move cautiously.

1. High capital intensity and total cost of ownership

Automation still shows up as a big line item rather than an operating tweak. Beyond the sticker price of robots, AMRs, conveyors, and vision systems, manufacturers must fund safety enclosures, upgrades to PLCs (programmable logic controllers), network infrastructure, and integration engineering that can easily double project costs.

For SMEs, this clashes with thin margins and conservative lending, especially in cyclical sectors like automotive or construction materials. Even where financing is available, boards now expect robust payback within two to three years, not open-ended “strategic” investments.

Warehouses face similar dynamics: integrating ASRS (automated storage and retrieval systems) or AMRs (autonomous mobile robots) into brownfield facilities means retrofitting racking, conveyors, and WMS (warehouse management system) interfaces, which pushes total cost of ownership far beyond brochure estimates. As a result, many operators default to incremental investments — low-cost sensors, semi-automated work cells, or leasing models — rather than committing to fully robotic lines.

2. Integration headaches with legacy infrastructure

Most factories are not greenfield “lights-out” showcases but a patchwork of older CNCs, bespoke fixtures, and partially digitalized lines. Connecting new robots, AGVs, and vision systems into this environment often requires custom mechanical design, nonstandard I/O mapping, and middleware to bridge incompatible protocols.

Integration firms report that for complex lines, engineering and commissioning can exceed the hardware cost, especially when you add parts presentation systems, safety redesign, and line balancing. Downtime is another deterrent: taking a profitable line offline for weeks to reconfigure conveyors, change layouts, and debug PLC logic is a hard sell to operations leaders.

In warehouses, AMR and shuttle deployments must sync reliably with existing WMS and ERP stacks, which often lack clean APIs or real-time inventory accuracy. These integration challenges are amplified in multi-site rollouts, where each plant has unique legacy equipment and tribal process knowledge.

3. Workforce resistance and skills constraints

Automation is as much an organizational change program as a technology investment. Yet many manufacturers underestimate the cultural and skills transformation required to move from manual or semi-automated workflows to robot-centered operations.

Operators and supervisors worry, often legitimately, that new systems will reduce headcount, compress overtime, or shift power toward central engineering. Surveys show sizeable pockets of employees who fear job displacement or lack confidence in their ability to master programming, troubleshooting, or data-driven decision-making.

Unions can slow approvals until retraining commitments, redeployment paths, and safety assurances are codified. At the same time, there is a chronic shortage of technicians and engineers who can design, maintain, and optimize robotic cells, particularly in smaller regional markets.

This creates a double bind: plants need skilled people to deploy automation, but they are pursuing automation partly because they cannot hire enough skilled people. Without deliberate communication, inclusive planning, and funded upskilling programs, resistance at the shop-floor level frequently converts strategic enthusiasm into stalled pilots or half-implemented systems.

4. ROI uncertainty in volatile, high-mix environments

On PowerPoint, automation RoI looks straightforward: higher throughput, fewer defects, lower labor cost. On the plant floor, the math gets messy. Demand volatility, SKU proliferation, and frequent engineering changes mean robots are rarely running the same process, at the same volume, for long stretches of time.

In high-mix, low-volume environments — common in job shops, contract manufacturing, and specialty warehouses — reprogramming robots, redesigning grippers, or reconfiguring ASRS logic can erode gains and extend payback timelines well beyond initial projections. Executives also factor in hidden costs: performance ramp-up, debugging, training, spare parts, software subscriptions, and cybersecurity.

Many report past experiences where early automation projects under-delivered, creating institutional skepticism about vendor ROI claims. Warehouse operators, likewise, worry about committing to systems that may not flex easily with omnichannel peaks, seasonal promotions, or changes in packaging and order profiles. In this context, maintaining human-driven flexibility can feel pragmatic.

5. Reliability, serviceability, and operational risk

For plant managers accountable to uptime KPIs, automation introduces a new class of single points of failure. A stalled palletizing robot, misaligned vision sensor, or frozen WCS (warehouse control system) can halt an entire line or warehouse zone, with cascading impacts on delivery SLAs.

In many regions, especially outside major industrial hubs, access to certified service technicians and rapid-response support is limited, extending mean time to repair when issues arise. Even with remote diagnostics, plants often need on-site intervention for mechanical failures, safety interlock problems, or PLC-level bugs.

Predictive maintenance, digital twins, and real-time monitoring promise better resilience, but these tools themselves require data quality, integration, and specialized skills that many mid-market manufacturers are still building. Past experiences with brittle automation — systems that function well only under ideal conditions and fail unpredictably under dust, temperature swings, or product variation — reinforce caution.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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”.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

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.

Subscribe now

Humanoids have begun to step into factories and warehouses in scenarios closer to real-world applications. Earlier humanoid robot deployments were largely confined to demonstrations, but the gap between lab and factory floor is closing. Recent high-profile deals such as the one between Toyota and Agility Robotics reflect the trend. Toyota Motor Manufacturing Canada has signed a Robots-as-a-Service agreement to deploy seven of Agility’s humanoid robots in manufacturing at its Woodstock, Ontario facility. Agility has also expanded its customer base in e-commerce, including a warehouse automation push by Mercado Libre in Latin America.

At BMW Group’s Spartanburg plant in the US, over ten months in 2025, Figure AI’s Figure 02 robot worked alongside human employees on the BMW X3 production line, completing approximately 1,250 operating hours and moving more than 90,000 components. The robot handled precise positioning of sheet metal parts for welding — a task requiring millimeter-level accuracy while being physically demanding for human workers. Within that period, it contributed to the production of 30,000 vehicles.

BMW has just announced that it is bringing robots and physical AI to some of its German plants.

These deployments also reveal how humanoids are being used currently. All involve structured, repetitive tasks in controlled environments. Material handling tasks dominate, including moving bins, positioning components, transporting totes between stations and so on. No manufacturer is deploying humanoids for assembly operations, welding cells, or machine tending — tasks that would require greater dexterity and adaptability.

Humanoid robot costs and ROI

The case for humanoid robots is gradually but increasingly supported by the math. A manufacturing worker in the US costs approximately $160,000 annually when wages, benefits, overhead, and payroll taxes are included. Current humanoid robot prices range from $13,500 for basic models like the Unitree G1 to $250,000 for more capable systems like Agility’s Digit.

At these prices, the payback period for replacing a single-shift worker ranges from two months for the least expensive models to nineteen months for premium units.

Multi-shift operations improve the economics further. A robot operating sixteen hours daily can effectively replace two workers at a combined cost of $320,000 per year. Maintenance, software updates, and integration costs add 50-100 percent to the purchase price, but the basic calculation remains attractive for high-wage environments.

In regions where labor costs are lower, or where existing automation solutions remain cost-effective, the business case weakens. The appeal of humanoid robots derives partly from their ability to work in facilities designed for human workers, avoiding the expense of redesigning production lines. But this advantage matters most in brownfield sites with space constraints or frequent reconfiguration needs.

The bill of materials for a humanoid could drop from the current $40,000 to $50,000 to $10,000 to $20,000 by 2035, the consultancy Bain & Co. projects.

Technical limitations of current humanoid robots

Current humanoids excel at mobility and basic manipulation in mapped environments but struggle with fine motor tasks requiring tactile feedback. Battery life remains limited — most industrial humanoids operate for 4-8 hours before requiring recharge. Reliability under continuous operation is improving but not yet proven over multi-year timeframes.

Safety certification for fenceless operation alongside human workers represents another hurdle. Most current deployments operate in semi-segregated zones or during off-hours when human presence is minimal. Agility Robotics has stated its next-generation Digit will be the first cooperatively safe humanoid capable of working directly alongside people at scale.

BMW notes that while motion sequences trained in the laboratory transferred to production faster than expected, early involvement of production IT infrastructure, occupational safety, process management, and shop floor logistics teams proved essential. Integration with existing manufacturing execution systems and warehouse management platforms requires substantial engineering effort.

Bain and Company’s research indicates that humanoid adoption will occur in three waves: industrial applications in automotive, mining, and construction first; then commercial uses in cleaning, healthcare, and hospitality; finally, consumer applications in domestic settings. The trajectory depends on technology maturation but also on measurable return on investment and building user risk tolerance.

From pilots to scale

The current landscape features many pilot programs, partnerships and testing agreements. It remains to be seen how many of those will transition to commercial deployments of hundreds and then thousands of humanoid robots.

Manufacturing executives require proof of reliability over extended periods — preferably years, not months. They need evidence that humanoids can match the uptime of existing automation solutions, which often exceed 95 percent. Questions about service and support networks, spare parts availability, and long-term vendor viability weigh heavily in capital equipment decisions of this magnitude. For context, a transition from pilot to meaningful scale would likely mean fleets of 50 or more units operating continuously across multiple shifts, a threshold no manufacturer has publicly reached with humanoid robots as of 2025.

The industry also faces a coordination challenge. Humanoid robots, unlike most other manufacturing technologies, lack established supply chains with tiered manufacturers building standardized modules. Each company develops its own actuators, control systems, and AI models. Some suppliers are beginning to address this by developing standardized humanoid components for use across multiple platforms. This vertical integration may accelerate innovation but creates vendor lock-in and complicates maintenance for end users operating multi-vendor fleets.

Humanoid robots are entering commercial use, but years away from scale deployment in most manufacturing environments. Even as they are being used for genuine industrial applications, deployments at scale are some years away.

Widespread adoption will require significant cost reductions, demonstrated multi-year reliability in production settings, expanded dexterity for complex manipulation tasks, and standardized safety certifications for human-robot collaboration. Progress on all fronts is happening, but unevenly.

Subscribe now

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.

Related posts

Subscribe now