(MENAFN- Robotics & Automation News) Awkward question: What will be the 'killer app' for humanoid robots?

October 22, 2025 by Sam Francis

Tesla's latest humanoid demonstration – a kung fu routine performed by its Optimus robot for actor Jared Leto – was clearly designed to impress.

But, as dramatic and as thought-provoking as it was, the spectacle raised an awkward question: what will be the“killer app” for humanoid robots?

“Killer app” is a phrase to mean the defining use case that makes a technology indispensable. In every major technology shift, there's been a defining use case – a killer app – that turned novelty into necessity. For early personal computers, it was spreadsheets – programs like VisiCalc and later Excel that justified owning a machine running DOS.

In mobile technology, the BlackBerry transformed from a curiosity into a corporate essential by allowing executives to read and send email on the move – a simple function that reshaped global communication.

Humanoid robots haven't yet found their equivalent. They can walk, wave, and even fold laundry in promotional videos, but they still lack that single, indispensable purpose that would make one worth buying.

VisiCalc was the first 'killer app' for the desktop computer. Image courtesy of SpaceBar

It won't be fighting or dancing – the market already has enough spectacle. The“killer app” for humanoids, if it ever arrives, will have to combine real-world utility, human trust, and price accessibility.

That goal remains elusive, and some researchers warn that humanoids are still fragile, easily hacked, and technically over-promised. Yet competition is fierce – and as history shows, breakthroughs often follow hype.

Humanoid evolution

Humanoid robotics has entered a phase that looks a lot like the personal-computer boom of the 1980s: a rush of companies, a flood of prototypes, and very few clear use cases.

Every week, new names join the race – from Tesla, Figure AI, and Agility Robotics in the United States to Unitree, Fourier Intelligence, and Noetix in China.

Each promises a new dawn of“general-purpose” robots able to work, assist, or even entertain. Yet most of these machines share the same fundamental limitations: they can walk and balance impressively, but they still struggle with dexterity, situational awareness, and the simple tasks that humans find trivial.

The question facing engineers and investors alike is whether any of these projects will discover the breakthrough – the“killer app” – that justifies the existence of humanoids beyond research labs and marketing videos.

It might be a household robot that reliably folds laundry and washes dishes, or an industrial helper that can handle unpredictable warehouse work without retraining. Or it might be something no one has yet imagined.

Until that moment arrives, humanoids remain in what one researcher called the“demo age” – capable enough to impress, but not indispensable. The next few years will determine whether they evolve into practical tools or remain spectacular curiosities on the frontier of automation.

Differentiation calculus

The humanoid boom has quickly turned into a crowded marketplace. Tesla's Optimus, Figure 01, Agility Robotics' Digit, 1X's Eve, Fourier's GR-1, and Unitree's H2 all share a similar blueprint: bipedal locomotion, a pair of dexterous arms, camera-based vision, and electric actuators.

Many even resemble each other physically, as though converging on a single design language – silver limbs, smooth faces, and minimalist joints.

Under the surface, their architectures aren't wildly different either. Most rely on torque-controlled electric motors, lightweight aluminum or carbon-fiber skeletons, and deep-learning-based control systems trained in simulation.

Software may be the new battleground, but even there, progress often looks iterative rather than revolutionary. As Rodney Brooks, co-founder of iRobot, has said,“We keep reinventing the same robot.”

A few companies have tried to differentiate themselves. Figure AI built its reputation on demonstrating household chores – folding laundry, tidying tables – tasks that remain elusive for most humanoids because of their demand for fine motor control and tactile precision.

Agility's Digit focuses on logistics, optimized for moving boxes in structured warehouse environments. In China, Unitree, Noetix, and Xiaomi have made humanoids the centerpiece of national robotics ambitions, each blending agile motion with consumer-level pricing strategies.

Despite the variety of brands, the reality is that humanoids still lack a defining purpose. They can walk faster, fall less, and look better than their predecessors – but they haven't yet answered the one question that will decide their future: what exactly are they for?

The first true BlackBerry wasn't even called a BlackBerry. Image courtesy of PC Mag Break on through to the humanoid side

The next genuine step change in humanoid robotics won't come from better balance or faster walking – those problems are largely solved. It will come from dexterity, perception, and autonomy: the ability to understand the physical world well enough to act within it safely and usefully.

Consider something as mundane as folding a towel or ironing a shirt. These are trivial for humans, yet technically brutal for machines. Laundry is soft, deformable, and unpredictable – the opposite of what industrial robots were designed for.

In research literature, this is known as“deformable-object manipulation,” and it remains one of the hardest challenges in robotics. A 2024 Science Robotics paper described it as the“frontier of embodied intelligence,” requiring vision, touch, and motion planning to work in harmony.

Companies such as Figure AI and Agility Robotics are already using simulated environments to teach robots complex skills thousands of times faster than real-world trials.

Nvidia's Isaac Sim and DeepMind's MuJoCo platforms enable humanoids to practice coordination and perception at scale, generating the data needed to refine their control policies.

Progress may also depend on shared learning – a foundation model for robot motion that, like large language models, can be fine-tuned by individual companies. Such a model could accelerate the entire field, allowing innovation to spread horizontally rather than being locked inside corporate silos.

Until then, every new humanoid is still a one-off experiment – walking, waving, and waiting for its breakthrough.

More simulated worlds to conquer

If real-world experimentation is too slow and costly, the next frontier for humanoids lies in worlds that don't physically exist.

Robotics companies are increasingly relying on digital twins and simulated environments to accelerate development, using them the way software developers use code sandboxes – to test, fail, and iterate at scale.

Platforms like Nvidia's Isaac Sim, DeepMind's MuJoCo, and Unity's Simulation Pro allow engineers to replicate entire laboratories, warehouses, or homes in virtual space.

Within these digital worlds, humanoid robots can attempt millions of grasping motions, walking cycles, or manipulation tasks in a fraction of the time that physical testing would require.

For companies such as Figure, Agility, and Sanctuary AI, this is becoming the invisible backbone of progress. Motion data generated in simulation trains the robots' control networks before they ever touch the floor.

Once transferred to the real machine, these policies can be fine-tuned through reinforcement learning – a feedback loop between simulation and physical performance.

The approach mirrors how AI systems like ChatGPT or AlphaFold were trained: vast, parallelized learning that converts trial and error into intelligence.

Some researchers even foresee a shared simulation infrastructure, where humanoids from different manufacturers could learn from overlapping data.

That vision remains distant, but it hints at an industrial-scale future – one where innovation is measured not by how many robots a company builds, but by how many worlds it creates for them to learn in.

China's hyperactive humanoid industry

If the United States and Europe are chasing humanoid robotics through venture funding and Silicon Valley startups, China is pursuing it through industrial scale and state ambition.

In the past year alone, at least half a dozen Chinese companies have unveiled full-scale humanoids, each promising to bridge the gap between research labs and commercial reality.

Unitree Robotics, known for its agile quadrupeds, has expanded aggressively into bipedal robots. Its new H2 humanoid boasts 31 degrees of freedom, 360 N·m joint torque, and near-human motion fluency.

Fourier Intelligence's GR-1 and Xiaomi's CyberOne follow a similar pattern – elegant frames, torque-rich actuators, and AI-driven motion algorithms – though most are still limited to demonstrations rather than deployment.

Noetix Robotics, one of the latest entrants, generated buzz with its low-cost model Bumi, priced under $1,400, aimed at mass-market education and R&D users.

Beyond the technology, China's humanoid push is supported by national policy. The Ministry of Industry and Information Technology listed humanoids as a strategic emerging industry, attracting local government subsidies and supply-chain alignment.

Domestic chipmakers, sensor firms, and motor suppliers are competing to become the country's preferred robotics components ecosystem.

What China lacks in first-mover advantage, it compensates with speed and volume. In a market where humanoids remain mostly conceptual elsewhere, China is already building supply chains for mass production.

Whether this leads to genuine breakthroughs or just more look-alike machines remains to be seen, but the sheer intensity of effort ensures that progress – or failure – will arrive faster there than anywhere else.

Early versions of Honda's Asimo humanoid robot walked very slowly and carefully, and probably would have fallen over if you even breathed on it. Image courtesy of Honda Global Step change

Every generation of humanoid robots seems to inherit the same dream – to walk, move, and behave like a human being. Two decades ago, that dream had a name: Honda's Asimo.

Standing just over a metre tall, Asimo could walk, run, climb stairs, and even carry a tray. At the time, it was a marvel of coordination and control, demonstrating that a bipedal machine could move without falling over.

But Asimo's gait was cautious, almost timid. It stopped, recalculated, and stepped gingerly – a reflection of how fragile balance control once was.

Today's humanoids, by contrast, stride with confidence, thanks to advances in torque sensing, dynamic balance algorithms, and lightweight actuator design.

Robots from Tesla, Agility, and Unitree can withstand pushes, recover from near-falls, and even run or jump.

Where Asimo was built around preprogrammed motion, modern humanoids rely on real-time computation and machine learning, adapting movement to their surroundings.

Some even use whole-body inverse dynamics, a concept borrowed from biomechanics, to maintain balance while interacting with objects or people.

And yet, despite the visual progress, the gap between motion and meaning remains. Asimo never understood what it was doing – and neither do most of its descendants.

They can move more smoothly and stay upright longer, but they still operate largely without context, performing choreographed actions rather than autonomous reasoning. The humanoid form may be perfected; the humanoid mind is still under construction.

Useful and affordable

For all the engineering brilliance poured into them, today's humanoids still lack a simple answer to the question of usefulness. They can perform set-piece demonstrations, but few can carry out repeatable, productive tasks outside the lab.

Even the most advanced prototypes – from Tesla's Optimus to Figure's 01 – still require close supervision and tightly controlled environments.

The turning point will come when a humanoid can complete a set of everyday jobs reliably and safely. Engineers talk about a viability threshold – roughly ten to fifteen practical tasks that a robot could perform without constant reprogramming.

Folding laundry, loading a dishwasher, unpacking deliveries, stocking shelves, or sweeping floors: individually mundane, collectively transformative. When a robot can do those consistently, it crosses from novelty to necessity.

Price will define the rest. Experts suggest that a humanoid must cost between $25,000 and $50,000 – about the same as a new car or a small suite of industrial equipment – before consumers or small businesses will seriously consider buying one.

Below that, adoption could accelerate sharply, particularly in ageing societies facing chronic labour shortages.

Some companies are already thinking along those lines. 1X Technologies and Fourier Intelligence have suggested target retail prices under $20,000 for future consumer models.

If they succeed, humanoids could move from trade-show curiosities to household appliances – expensive, perhaps, but no longer unthinkable.

The advantages of being human

Part of the enduring fascination with humanoid robots lies in their resemblance to us – and in the possibility that they could, one day, use the same world we do.

Our homes, factories, and cities are designed for human ergonomics: door handles, steps, power tools, kitchen appliances, steering wheels, and computer keyboards all assume a roughly human form.

That gives humanoids a built-in advantage. Unlike specialized robots that require custom hardware or tightly controlled environments, a humanoid can in theory use existing infrastructure.

It can open the same doors, drive the same forklifts, and hold the same tools. The promise is enormous: a single machine design that can adapt across workplaces without bespoke re-engineering.

This is one reason why investors and engineers remain so committed to the concept, despite its challenges.

The idea of a“general-purpose” robot that can operate anywhere humans can is more than technological ambition – it's economic logic.

Building a new class of robot for every task is expensive. Building one adaptable platform, however complex, could eventually prove cheaper.

There's also the emotional dimension. Humans respond instinctively to human-like motion, expressions, and gestures. The more humanoids resemble us, the easier it may be to trust them – an intangible but critical factor in adoption.

The irony, of course, is that imitation alone isn't enough. To earn that trust, humanoids will have to prove they can act not just like us, but for us.

Form follows function

For all the digital promise of humanoid robotics, there remains a stubborn reality: what a virtual robot can do effortlessly in simulation often collapses under the weight of physics in the real world.

A simulated hand can fold a shirt perfectly a thousand times in Isaac Sim; a physical robot still drops it on the floor. The jump from virtual mastery to mechanical reliability is one of the hardest engineering gaps to close.

That gap is what separates science fiction from utility. It's also what will determine which companies survive the humanoid race.

Spectacle – kung fu, backflips, runway walks – captures headlines but not markets. The breakthroughs that matter will be the quiet ones: better grip control, smoother motion planning, lower energy consumption, safer human interaction.

If history is any guide, a single“killer app” could change everything. Just as spreadsheets once justified owning a computer and mobile email sold millions of early smartphones, one compelling use case could turn humanoids from demonstrations into daily tools.

It might be in healthcare, logistics, or the home – the first robot that reliably helps rather than merely entertains.

Until that moment arrives, humanoids stand on the threshold between imagination and industry. They can already move like us; the question is when they will finally work like us – and for us.

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