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Rodney Brooks, the roboticist and iRobot cofounder, argues that humanoid-robot companies are underestimating a fundamental obstacle: dexterity depends on touch, and robots still need ways to sense, measure, record and learn from it. He also doubts that one humanlike design will be the best form for every job. Those are Brooks’s forecasts and criticisms—not proof that humanoid robots cannot improve or that his view will prevail.
Who is warning about humanoid robots—and what does “terrible surprise” mean?
The headline refers to Rodney Brooks, a roboticist and cofounder of iRobot, the company behind Roomba. In a blog post from September 2025, Brooks questioned whether large investments and training efforts can deliver the humanlike dexterity promised for current humanoid robots. Futurism reported on his argument on October 3, 2025. The “terrible surprise” is a punchy description of his skepticism, not a reported announcement of a specific setback or a demonstrated failure of Tesla’s Optimus program.
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Brooks’s criticism has two parts: robots need a better foundation for learning touch-dependent manipulation, and humanoid form may not be the right answer for every task. His argument is about engineering challenges and what kinds of robots may prove useful; it is not a consumer buying guide or a controlled comparison of robot products.
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Many everyday manipulations depend on more than seeing an object and moving a hand toward it. A person adjusts grip when an object slips, feels whether a surface is fragile, and responds to pressure and contact as a task unfolds. Brooks’s central point is that a robot cannot reliably learn all of that from visual demonstrations alone: touch must also be sensed and made usable as training information.
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In the quotation reproduced by Futurism, Brooks argued that teaching dexterity without understanding the components of touch, measuring tactile sensations, and storing and replaying touch information would be “probably dumb. And an expensive mistake.” He also wrote that current humanoid robots would not learn dexterity despite the substantial amounts of money being directed toward their training.
What the touch-data problem involves
- Sensing: A robot needs information about contact—such as pressure or slipping—to make adjustments that vision alone may not reveal.
- Measurement and representation: Contact information must be captured in a form the system can interpret and use, rather than treated as an unmeasured sensation.
- Reusable learning: If touch data can be stored and replayed, it can potentially contribute to training across attempts. Brooks’s argument is that this capability is a prerequisite worth solving, not a detail that money or more demonstrations automatically make disappear.
Fortune revisited Brooks’s September 2025 argument on February 25, 2026, and reported his observation that “we do not have such a tradition for touch data.” That supports the distinction he is making between visual training examples and tactile information; it does not establish that no robot can manipulate objects or that tactile sensing will remain unsolved.
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Why might a humanoid body be the wrong design?
A human-shaped robot has an intuitive appeal in places built for people: it could, in principle, reach objects and use tools designed around human bodies. But resemblance alone does not make a robot effective. A machine optimized for a particular environment or task may work better with a different way to move or interact.
Brooks forecast that useful robots will take varied forms for specialized jobs. In the quotation reported by Futurism, he suggested humanoids might acquire wheels in place of humanlike legs, and summarized his view this way: “There will be many, many robots with different forms for different specialized jobs that humans can do.” This is a prediction about design direction, not evidence that a particular wheeled robot has replaced a humanoid in a given job.
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Two competing design questions
| Question | Brooks’s critique | Humanoid ambition |
|---|---|---|
| How will the robot handle objects? | Dexterity depends on tactile sensing and data that can be measured, stored and replayed. | A humanlike body does not by itself demonstrate reliable dexterity; performance depends on the robot’s sensing, training and task results. |
| What body should it have? | Expect different forms for specialized jobs, potentially including wheels rather than humanlike legs. | A humanlike form aims to operate in environments and around tools designed for people. |
| What counts as evidence? | Brooks’s statements are an expert critique and forecast about technical and design challenges. | A demonstration, a working deployment and an economic projection are different kinds of claims and should be evaluated separately. |
What has Elon Musk claimed about Optimus?
Futurism’s October 3, 2025 report described Musk’s ambitions for Tesla’s Optimus as company-leader forecasts, not achieved results. It reported his projection of more than $10 trillion in long-term Optimus revenue and his claim that robots could account for 80% of Tesla’s value. Neither figure is an independently established estimate of future sales or company value.
The same report mentioned a 2025 production target of 5,000 Optimus robots and a reported shortfall, attributing the technical issue to The Information. That is historical reporting about a target and reported performance in 2025; it should not be read as a statement of current production or project status.
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How should readers judge the disagreement?
Brooks’s argument identifies questions that matter for any claim about a general-purpose humanoid: Can the robot manipulate objects reliably, what tactile and demonstration data support that ability, and does a humanlike body help with the actual environment and job? Those questions are more informative than a video demonstration or a revenue forecast alone.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Separate capability from ambition. A company’s stated goal or forecast is not proof that the robot can perform the intended tasks reliably.
- Look for task evidence. Useful comparisons would show what a robot can do, under what conditions, and how consistently—not merely that it completed a staged demonstration.
- Ask whether the form fits the work. Humanlike limbs may help in some human-designed environments; another configuration may better suit a specialized task.
- Keep the evidence limits in view. The Futurism and Fortune reporting presents Brooks’s critique and relevant company ambitions, but it does not provide controlled benchmark comparisons among Tesla Optimus and other humanoid robots, or establish a consensus among robotics experts.
What Brooks’s warning does—and does not—show
Brooks offers a concrete reason to question confident promises of humanlike dexterity: touch is a sensing and data challenge, not simply a matter of adding more training. His prediction that specialized robots will use varied forms also challenges the assumption that one humanoid design is the universal solution. Whether those concerns become the decisive limits on Optimus or the wider humanoid-robot industry remains unsettled by the reporting cited here.
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