Rodney Brooks, the roboticist who cofounded iRobot, argues that humanoid robots may hit a major obstacle before they become the capable, general-purpose workers their backers envision: machines still need a way to sense, measure, store and learn from touch. His warning is a critique of current ambitions, not proof that humanoid robots cannot become dexterous. It also points to a different possibility: robots designed around specific jobs may prove more useful than machines built to look and move like people.
What Brooks says is the hard part about humanoid robots
In a blog post from September 2025, Brooks focused on tactile sensing—the information a robot gets through physical contact. Futurism quoted his view on Oct. 3, 2025: “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.”
The point is not simply that a robot needs sensors on its hands. Dexterous work depends on interpreting contact well enough to act and adjust. A robot must be able to learn from touch, and its training process needs usable touch information to capture and reproduce. Brooks argues that spending heavily on robot training does not, by itself, solve that underlying sensing and data problem. He wrote that current humanoids would not learn dexterity merely because hundreds of millions—or perhaps billions—of dollars were being put toward training.
Why touch data matters
Training can only teach what its examples and feedback make available. If touch is not meaningfully measured and represented, a robot may lack information needed to learn how contact changes during manipulation. Brooks’s criticism is therefore about the foundations of dexterity, not a claim that more computing or investment is inherently useless.
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Fortune revisited the argument on Feb. 25, 2026, and quoted Brooks saying, “we do not have such a tradition for touch data”. That captures the gap he sees: the ability to collect and use tactile information at scale cannot simply be assumed because robots can already be trained with other kinds of data.
Why a human-shaped robot may not be the best tool for every job
Brooks also challenges the idea that one humanoid body is the natural form for a general-purpose robot. A human shape can be useful where robots must operate in spaces and around tools designed for people. But that does not establish that humanlike legs, hands or proportions are the best design for every task.
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His forecast is that useful robots will take different forms for different work. Futurism quoted him predicting that humanoids could acquire wheels for feet, first two and later perhaps more, until their lower bodies no longer resemble human legs. He summarized the broader view this way: “There will be many, many robots with different forms for different specialized jobs that humans can do.” These are Brooks’s predictions, not a settled account of what the robotics market will become.
The design question is practical: does a humanlike body help a robot do the job in its actual environment? A robot intended to use human infrastructure may benefit from humanlike reach or mobility. A machine assigned a narrower task may work better with a different way of moving or handling objects. Brooks’s argument is that engineers should not treat resemblance to people as a substitute for matching a robot’s form to its work.
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How Brooks’s critique differs from Musk’s Optimus ambition
The disagreement is between an engineering warning and a commercial vision—not between a completed product and a proven failure. Brooks emphasizes the unresolved challenge of touch and questions whether a single humanoid form is the right answer. Elon Musk has presented Optimus as a major future business opportunity for Tesla. The reporting does not provide controlled benchmark comparisons that establish how Optimus performs against other humanoids or specialized robots.
| Question | Brooks’s critique | Musk’s Optimus ambition as reported |
|---|---|---|
| What needs to be demonstrated? | Reliable dexterity requires understanding, measuring, storing and replaying touch information. (Brooks, quoted by Futurism, Oct. 3, 2025.) | Optimus is presented as a large future opportunity; the reporting does not establish that the projected capability or market has been achieved. |
| What should robots look like? | Different forms may suit different specialized jobs; some robots may use wheels rather than humanlike legs. | The ambitions discussed center on Tesla’s humanoid Optimus, but the cited reporting does not establish that a humanoid is best for every task. |
| What is the evidence? | A technical argument and forecast by Brooks, not a controlled market-wide evaluation. | Company-leader projections and production reporting, not independent proof of long-term economics or general-purpose performance. |
What the reported targets and projections do—and do not—show
Futurism’s Oct. 3, 2025 article attributed to Musk projections of more than $10 trillion in long-term Optimus revenue and a scenario in which robots could account for 80% of Tesla’s value. These were forecasts, not measured revenue or an established valuation. The same report described a Tesla target of 5,000 Optimus robots for 2025 and a reported shortfall, attributing the technical issue to The Information. Those dated reports do not establish Tesla’s current production or project status.
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Targets and market projections can communicate a company’s ambitions; they cannot settle Brooks’s technical concern. To assess whether humanoids are becoming useful, readers need evidence about what tasks robots can perform reliably, what tactile sensing and training data support that performance, and whether the body design suits the job. A public demonstration, a working deployment and a profitable business are different milestones.
What would resolve the disagreement
Brooks’s warning should be taken seriously as an expert critique, but not mistaken for a consensus statement or a demonstrated limit that no future system can overcome. The reporting cited here does not supply head-to-head tests of Tesla Optimus, other humanoids and task-specific robots, so it cannot determine which approach performs best.
More useful evidence would show whether robots can handle varied physical tasks reliably outside staged demonstrations, how touch is sensed and represented during training, and how performance changes across jobs and environments. It would also show whether the humanoid form provides a practical advantage over a specialized design for each task. Until that evidence is available, Brooks’s “terrible surprise” is best understood as a warning about the difficulty of dexterity—and the risk of treating an ambitious forecast as an engineering result.
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