Rodney Brooks, the roboticist and iRobot cofounder, argues that today’s humanoid robots face a fundamental obstacle: machines still lack the touch sensing and touch data needed to learn humanlike dexterity reliably. He also doubts that one human-shaped machine will be the best tool for every job. That is a prominent expert’s critique and forecast—not proof that humanoid robots cannot improve, or that a particular company’s plans will fail.
What is Brooks warning about?
In an October 3, 2025, report, Futurism described Brooks’s argument in a blog post from the previous month. His concern is not simply that robots need more training. It is that dexterity depends on touch, and the information involved in touch is difficult to capture and use as training data.
Brooks put the concern bluntly: “To think we can teach dexterity to a machine without understanding what components make up touch, without being able to measure touch sensations, and without being able to store and replay touch is probably dumb. And an expensive mistake.” Fortune revisited the argument on February 25, 2026, and reported Brooks saying, “we do not have such a tradition for touch data”.
Why does touch data matter for dexterity?
Vision can help a robot identify an object and where it is. But manipulating it also means responding to contact: how firmly to grip, whether an object is slipping, and how to adjust when its shape or resistance differs from expectation. Those are examples of why touch is relevant to dexterity; Brooks’s central point is that a system needs touch information it can sense, measure, store, and use for learning.
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Collecting demonstrations or spending more on training does not, by itself, establish that a robot has learned those skills. The important question is whether the robot can use tactile information to perform a task reliably—not merely whether it can reproduce a movement in a demonstration. Brooks’s claim that current humanoids will not learn dexterity despite substantial investment is his assessment, not a controlled comparison of robot systems.
Why might useful robots stop looking human?
A humanlike body can make sense in spaces and workplaces designed for people, where existing tools, controls, and paths may suit a person’s reach and movement. But resemblance is not automatically an engineering advantage. A different job may be done better by a machine with a different way to move or handle objects.
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Brooks predicts a shift toward specialized designs rather than one humanoid form as an all-purpose assistant. Futurism quoted him forecasting that some humanoids may get wheels for feet, with forms that no longer closely resemble human legs, and that many robots will have different forms for different specialized jobs. This is a prediction about where useful designs may go, not an established market outcome.
What do Musk’s Optimus forecasts establish—and what do they not?
Futurism’s October 2025 article attributed ambitious financial expectations for Optimus to Elon Musk. Those expectations should be read as forecasts, not as achieved revenue, proven economics, or independently validated estimates.
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| Reported figure | What it refers to | How to read it |
|---|---|---|
| More than $10 trillion | Long-term Optimus revenue projected by Musk, as reported by Futurism in October 2025 | A company-leader forecast; the report does not establish it as realized revenue or an independently supported estimate. |
| 80% of Tesla’s value | The share of Tesla’s value Musk reportedly said robots could represent, in Futurism’s October 2025 report | A forecast about potential value, not evidence that robots already account for that share. |
| 5,000 robots in 2025 | A production target reported by Futurism in October 2025 | The same report described a shortfall and attributed a technical issue to The Information. This is a dated report, not a statement of current production status. |
For a finance-minded reader, the distinction is between a large potential market and evidence that a product can do valuable work at scale. A revenue projection does not answer whether a robot can complete tasks dependably, how much it costs to operate, or whether customers will pay enough to support the business. The cited reporting does not provide the data needed to settle those questions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should readers assess competing robot claims?
Public demonstrations, production targets, and business forecasts answer different questions. To assess claims about humanoids—including Optimus—look for evidence on the specific capability and the conditions under which it was shown.
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- Dexterity: What objects and tasks can the robot handle, and how consistently?
- Touch: What tactile sensing does it use, and what evidence shows that the robot can learn from touch information?
- Training: What kind of data supports the task, and does performance hold beyond the examples used to train or demonstrate it?
- Form: Does a humanlike body help with this job and environment, or would a specialized design be more suitable?
- Deployment: Is the evidence a staged demonstration, a production target, or repeated work in an operating setting?
- Economics: Are there disclosed costs and customer results, or only forecasts about future revenue and value?
The Futurism and Fortune reports present Brooks’s critique, not a systematic evaluation of Tesla Optimus against robots from Figure, Unitree, or other developers. They therefore support understanding the argument, but not ranking current products or declaring which business will succeed.
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