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XPeng Iron humanoid robot brings humanoids into car factories

7 Nov 2024 | Robots | 0 comments

Humanoid robots are no longer just lab projects or sci‑fi props. XPeng Iron humanoid robot is already turning screws and moving parts on real car assembly lines in China. By pairing this robot with its Kunpeng super electric system and in‑house AI chips, XPeng is trying to build one shared brain and power system for cars, robots, robotaxis, and even flying vehicles.

If this strategy works, it could speed up factory automation, give XPeng an edge in the global EV race, and change how people work with machines on the factory floor.

How XPeng Iron humanoid robot works on the factory floor

XPeng built Iron to roughly match human size and reach so it can use tools, handle parts, and move in spaces designed for people, as shown in its 2024 AI Day reveal of the Iron humanoid robot and Kunpeng system. Unlike classic industrial robot arms fixed to one spot, Iron is made to walk through the plant and share work cells with human staff.

Body design and movement capabilities

The first public version of Iron is about 178 cm tall and weighs around 70 kg, close to an average adult man. It has more than 60 joints and roughly 200 degrees of freedom, meaning its joints can move in many independent ways so it can bend, twist, and reach in a human‑like fashion.

Iron’s legs include hip, knee, and ankle joints that give it a stable bipedal walk. Its torso has a flexible spine that lets it lean and twist to reach into tight spaces. The arms include shoulders, elbows, and wrists with enough travel to lift tools, move boxes, and work at different heights. Iron’s hands are small and simple compared with a human hand, but its dual‑finger grippers can still hold parts, tools, and boxes with a range of grips.

Later “next‑gen IRON” prototypes shown in 2025, including the version presented at XPeng’s 2025 AI Day with more than 60 joints and around 200 degrees of freedom, use an internal skeleton and artificial muscle system that makes movements smoother and more lifelike. For readers, the key point is that each new version is moving closer to human motion while keeping a machine‑like endurance.

AI brain, vision system, and training

Instead of buying off‑the‑shelf chips, XPeng developed the Turing AI chip that powers both its cars and Iron. The chip is built to run large AI models at very high speed, reaching thousands of trillions of operations per second. That level of compute is what lets the robot process camera images, understand instructions, and plan motions almost in real time.

Iron’s “eyes” are a 720‑degree vision system: a set of cameras arranged so the robot can see all around itself. XPeng relies on a pure‑vision approach similar to the one it uses in its driver‑assist and robotaxi systems. Instead of depending on laser scanners or detailed pre‑made maps, its models learn to parse raw camera images into depth, objects, and free space.

On top of that, XPeng is building a Vision–Language–Action (VLA) model that links what Iron sees, what it is told to do, and the movements it chooses. The company says the same type of model helps its robotaxis convert descriptions of the scene into driving actions. Training these models on driving and factory footage lets Iron learn from millions of real‑world examples, not just from simulated data.

Current roles for Iron in XPeng factories

XPeng has already shown Iron doing real assembly work. In videos from its factories and trade shows, Iron picks up parts from bins, holds tools such as power screwdrivers, and installs fasteners on car bodies. It cooperates with mobile robots that bring materials to the line, then hands off finished parts for the next step.

These early tasks are narrow but important. For XPeng, a humanoid that can walk into an existing workstation and handle screw tightening, parts sorting, or material handling could cut the need to redesign lines around fixed robot arms. Over time, the same basic robot body could be re‑trained or re‑tasked as products and workflows change.

XPeng Iron and the global race for humanoid robots

XPeng is not alone. Tesla’s Optimus, Figure’s Figure 01, and several Chinese startups are all racing to show factory jobs done by walking robots. Iron enters this race with a few unique advantages: a direct link to a carmaker’s large factory network, and a shared chip and software stack with XPeng’s vehicles.

How Iron compares with other factory humanoids

On paper, Iron’s basic dimensions are similar to Tesla’s Optimus and other human‑scale robots: human height, tens of kilograms in weight, and dozens of joints. Its compute budget, measured in the low thousands of TOPS, is also in line with other high‑end platforms.

Where XPeng tries to stand out is in its use of the same Turing AI chip and pure‑vision models across cars, robots, and robotaxis. Rather than design different computers for each product, XPeng is betting that one flexible AI stack can be trained once and deployed many times. That could lower costs and shorten development cycles if it works as planned.

Why carmakers want humanoid robots in their own plants

For car companies, labor is a major cost and bottleneck, especially for tasks that are repetitive, physically hard, or slightly dangerous but still need human‑like dexterity. Classic industrial robots handle welding and painting well, but many final‑assembly jobs still fall to people.

Humanoid robots promise to take on some of these tasks without tearing up and rebuilding the plant. A robot with human‑like reach, ability to climb stairs, and use hand tools can in theory walk into a workstation designed for a person and do a similar job, with fewer changes to the line. If a carmaker owns the robot platform, it also gains more control over data, safety rules, and long‑term costs.

XPeng’s move fits a wider pattern that metameha has covered in other contexts, such as how an AI system enabled a 20‑minute interactive exchange with a humpback whale. In each case, companies try to turn AI models trained on large streams of sensor data into practical systems that act in the physical world.

Limits and open problems for humanoid robots

Despite the buzz, humanoid robots still face clear limits. Battery energy is constrained, especially when a robot walks and carries loads for long shifts. Even with efficient motors and high‑voltage batteries, it is hard to match human endurance.

Manipulation skills also lag behind humans. Iron’s hands are far less capable than a real hand, so many tasks still need custom grippers or human workers. Safety is another concern. A 70 kg robot moving near people must detect contact, respond quickly, and obey strict rules so it does not injure co‑workers.

Finally, cost remains a barrier. Until robots like Iron can be built, maintained, and updated cheaply enough, they will be limited to pilot projects and high‑value tasks. XPeng’s hope is that sharing chips and software with high‑volume EVs will help bring costs down.

Kunpeng super electric system links XPeng cars, robots, and AI

At the same AI Day where Iron debuted, XPeng also launched the Kunpeng super electric system. This is not a single car, but a high‑voltage platform that aims to give XPeng long‑range EVs with very fast charging and better efficiency.

How Kunpeng’s high‑voltage tech works in XPeng EVs

Kunpeng is built on an 800 V silicon‑carbide platform. In simple terms, this means the main power electronics use silicon carbide instead of standard silicon, which wastes less energy as heat when handling large currents. The system includes a 5C “AI” battery pack that can accept very high charge rates, a hybrid silicon‑carbide coaxial drive unit, and a quiet range‑extender engine in some models.

In new extended‑range cars like the XPeng P7+ EREV, XPeng says Kunpeng can deliver around 430 km of pure‑electric driving and up to 1,400 km of combined range when the range extender is used. Fast chargers tied into this system can reportedly bring the battery from 0 to 80% in roughly 12 minutes, which is close to adding about 1 km of range per second on the gauge.

Shared chips and software between EVs, robots, and robotaxis

Kunpeng shows how XPeng wants its EVs, robots, and robotaxis to share not just power hardware, but also computing and AI. The same Turing AI chips that run autonomous driving features in XPeng cars also power Iron, and will sit at the heart of XPeng’s planned Level 4 robotaxis.

This shared stack matters. Data from cars on the road can help improve the models that control Iron’s walking, grasping, and obstacle avoidance. Lessons from robots working in chaotic factory spaces can feed back into how robotaxis handle crowded depots or loading areas. Over time, this web of shared data can make all three product lines more robust.

Other tech moves fit the same pattern. For example, metameha has covered how virtual avatars in VR can change how the brain perceives the body. XPeng’s vision is similar in spirit: one AI core that can “inhabit” different bodies, from a sedan to a flying car to a humanoid worker.

What Kunpeng and Iron say about the future of “physical AI”

XPeng’s leaders describe this as a shift toward “physical AI,” where intelligent systems are tightly tied to the machines that move people and goods. Instead of treating cars as simple vehicles and robots as separate tools, the company treats all of them as embodiments of one underlying AI system.

If the strategy works, XPeng could lower costs and speed up new products, because improvements in one area, such as perception or motion planning, would spread quickly to others. If it fails, however, XPeng risks tying too many bets to one stack, which could be hard to update or replace if a better standard appears.

What XPeng Iron humanoid robots could do next in daily life

Today, Iron’s work is mainly inside XPeng’s plants and showrooms. But XPeng and several media outlets already talk about future roles for humanoids in public and private spaces. These visions are early and may take years to arrive, yet they signal where companies hope the market will go.

Possible roles for Iron beyond the factory

In the near term, XPeng suggests that Iron or its successors could stand at reception desks, guide visitors through showrooms, or act as brand ambassadors at auto shows. In logistics settings, humanoids might handle tasks that require climbing steps, opening doors, or working in tight mixed‑use spaces where installing fixed conveyors and robots would be too costly.

Further into the future, XPeng hints that home helper roles might be possible: simple household chores, basic elder support, or night‑time security patrols. Those ideas overlap with a broader trend of companies pitching humanoids as general‑purpose helpers rather than single‑task machines.

Jobs, safety, and regulation for humanoid robots

If humanoid robots spread beyond factories, the main questions will not be technical but social. People worry, with reason, about robots taking jobs in warehouses, retail, or cleaning. The shift may not remove work altogether, but it could change which skills are in demand.

Safety is another core issue. Laws and standards will need to address how heavy mobile robots behave around children, older adults, or crowds. Rules might cover speed limits in shared spaces, required safety sensors, and how companies must report incidents.

Governments are only starting to think about detailed rules for humanoid robots. For now, companies like XPeng rely on internal safety policies and broader machine‑safety standards. As deployments move from controlled factory floors into malls, offices, or streets, public regulators will likely push for stronger oversight.

How to make humanoid robots useful and trusted

For robots like Iron to be accepted, they must be both useful and predictable. That means clear, simple roles; easy‑to‑understand behavior; and transparent limits. Design choices that make robots look less threatening, such as smooth motion, clear status lights, and friendly but not uncanny faces, can also help.

Cost will matter too. Only when the total cost of ownership undercuts a mix of human labor and simpler machines will customers adopt humanoids at scale. XPeng hopes that building Iron on top of its EV platforms and AI chips will lower costs enough to cross that threshold.

Sources & related information

XPENG – XPENG unveils Kunpeng super electric system and AI‑defined mobility innovations at XPENG AI Day – 2024

XPeng’s official press release on Kunpeng and Iron describes how the company introduced the Kunpeng super electric system, the Turing AI intelligent driving system, and its humanoid AI robot Iron at XPeng AI Day 2024, framing them as parts of a unified AI‑defined mobility strategy.

CarNewsChina – Xpeng showcases humanoid robot Iron used for making its vehicles – 2025

A report from CarNewsChina on Iron in XPeng’s factory covers XPeng’s Iron robot at the 2025 Shanghai Auto Show, noting its 60 joints, 200 degrees of freedom, Turing AI chip with 3,000 TOPS of compute, and demonstrations of the robot performing assembly tasks in XPeng’s factories.

EVMoD – Xpeng showcases humanoid robot Iron used in EV production – 2025

Thai‑language coverage of XPeng’s AI Day reveal summarizes XPeng’s AI Day 2024 technology reveals and explains how Iron is designed for use on XPeng’s EV production lines, including specs such as its joint count, degrees of freedom, and use of XPeng’s own AI hardware.

TechNode – Xpeng targets 2026 with three Robotaxi models and mass‑produced humanoid robot IRON – 2025

TechNode’s feature on robotaxis and IRON outlines XPeng’s plan to launch three self‑developed Level 4 robotaxi models and mass‑produced IRON robots in 2026, all built around the company’s Turing AI chips and Vision–Language–Action model in a broader “physical AI” strategy.

LiveScience – Watch: Chinese company’s new humanoid robot moves so smoothly, they had to cut it open to prove a person wasn’t hiding inside – 2025

LiveScience’s story on the new IRON prototype reports on a newer XPeng IRON prototype presented at XPeng AI Day 2025, describing its lifelike movements, internal skeleton, artificial muscles, and over 80 degrees of freedom, as well as XPeng’s vision for IRON as a future “life companion or colleague.”

Gulf News – Meet ‘Iron’: Made‑in‑China humanoid robot with 200 degrees of freedom – 2024

The Gulf News article introducing Iron that prompted this article introduces Iron’s key specs, explains its role in XPeng’s factories, and briefly describes the Kunpeng super electric system and XPeng’s broader plans for robotaxis and smart mobility

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Listening is the underrated skill that makes you a better leader instantly

We often think of great leaders as great talkers. We imagine them giving rousing speeches, setting a clear vision, and having an answer for everything. But a massive review of scientific research suggests we have it backward. The most effective way to improve your leadership isn’t to speak more; it is to listen better.

New data shows that listening is not just a “soft skill” for making friends – it is a hard driver of job performance and professional success.

144 studies confirm listening drives performance

A recent meta-analysis published in the Journal of Business and Psychology examined the link between listening and work outcomes. The researchers looked at data from 144 studies involving more than 155,000 people.

Their conclusion was clear: listening has a strong, positive effect on employee job performance.

Leaders who are perceived as good listeners do more than just make their employees feel warm and fuzzy. They actually get better results. The study found that listening improves the quality of relationships at work, which in turn boosts performance. When employees feel heard, they perform better. This dynamic helps leaders unlearn bias and lower conflict within teams.

As the researchers noted, the link between listening and positive job outcomes is “robust.” They suggest that listening is an underrated predictor of job performance – a simple cause of superior results that many organizations overlook.

Why we love to talk about ourselves

If listening is so effective, why is it so hard? Why do so many of us default to talking instead?

The answer lies in our biology. A study published in the Proceedings of the National Academy of Sciences (PNAS) found that talking about ourselves is inherently rewarding. In fact, humans devote about 30–40 percent of everyday speech to informing others about their own subjective experiences – their thoughts, feelings, and opinions.

Using brain scans, researchers found that self-disclosure activates the mesolimbic dopamine system – the same brain regions associated with the pleasure we get from food, money, and sex. It feels good to talk about yourself.

The drive is so strong that people in the study were willing to give up money just to keep talking about themselves. When given a choice between answering questions about others for a higher payment or answering questions about themselves for a lower payment, participants voluntarily gave up between 17 and 25 percent of their potential earnings to talk about their own views.

We are wired to broadcast. To lead effectively, you have to fight that wiring.

The power of follow-up questions

You can become a better listener instantly by changing how you ask questions. It is not enough to just stay silent; you need to show you are engaged.

Research published in the Journal of Personality and Social Psychology found that the specific type of question you ask matters. The study showed that asking follow-up questions – questions that ask for more detail on what the other person just said – dramatically increases how likable you appear.

When you ask a follow-up question, you prove you were listening. You signal validation, care, and understanding. This simple habit makes you more persuasive and influential because, as other research in Frontiers in Psychology shows, likable people are better at influencing those around them.

Asking follow-up questions and recalling small details are among seven habits that mark an exceptional listener, and this research confirms it is a key tool for leaders.

Feeling known leads to feeling supported

Listening does more than build rapport; it meets a fundamental human need.

A study in the Journal of Personality and Social Psychology found that employees feel less objectified when their boss knows them as people, rather than just as workers or numbers. Furthermore, research linked to the Journal of Experimental Psychology suggests that “feeling known” is a necessary precursor to “feeling supported.”

You cannot support an employee you do not know. You cannot help them reach their career goals if you never asked what those goals were. You cannot solve their roadblocks if you never listened to what those roadblocks are.

What you can do about it

To become a better leader today, flip the ratio of your conversations.

  • Talk less. Recognize that your brain wants the dopamine hit of talking about yourself. Resist it.
  • Ask for their story, not yours. Instead of telling your team about your weekend or your problems, ask about theirs.
  • Use the follow-up rule. When an employee answers, do not just nod and move on. Restate what they said or ask one follow-up question based on what they just said.
  • Listen to learn. You already know what you know. The only way to learn something new is to listen to what others know.

Mastering conversation: how active listening keeps dialogue engaging is a skill you can practice in every interaction, whether with a colleague, a client, or a friend.

Sources & related information

Journal of Business and Psychology – The Power of Listening at Work – 2023

A meta-analysis of 144 studies involving 155,000 observations found that perceived listening is strongly correlated with improved job performance and relationship quality.

Proceedings of the National Academy of Sciences – Disclosing information about the self is intrinsically rewarding – 2012

Neuroimaging research shows that self-disclosure activates the brain’s reward systems, motivating people to talk about themselves even at a financial cost.

Journal of Personality and Social Psychology – It Doesn’t Hurt to Ask – 2017

A series of studies demonstrates that asking follow-up questions increases interpersonal liking by signaling responsiveness and listening.

The Pratfall Effect: why making mistakes can make you more likable

Perfection is often overrated. While we strive to be flawless in job interviews or first dates, psychology suggests that being too perfect can actually push people away. A small blunder, like tripping or spilling a drink, might do more for your popularity than a flawless performance. This phenomenon is known as the Pratfall Effect.

What is the Pratfall Effect?

The Pratfall Effect is a psychological principle that states that a person’s likability increases when they make a clumsy mistake, but only if that person is already perceived as competent.

Social psychologist Elliot Aronson first identified this effect in 1966. He wanted to test how mistakes influence attraction. In his famous experiment, he asked male college students to listen to tape recordings of people answering quiz questions.

The participants heard one of two main scenarios:

  1. The Superior Person: This person answered 92% of the questions correctly. They sounded confident and knowledgeable.
  2. The Average Person: This person answered only 30% of the questions correctly.

Aronson then added a twist. In some recordings, the “Superior Person” commits a blunder at the end: they are heard spilling a cup of coffee and reacting to the mess.

The results were clear. The students rated the Superior Person who spilled the coffee as the most likable of all. The blunder made the highly competent person seem more human and approachable.

The catch: competence is key

There is a crucial condition to this effect. A mistake only helps you if you have already established your competence.

In Aronson’s experiment, when the “Average Person” (who missed most quiz questions) spilled the coffee, their likability rating dropped even further.

  • If you are competent: A mistake humanizes you. It breaks the “too good to be true” barrier and prevents others from feeling threatened by your perfection.
  • If you are incompetent: A mistake just reinforces the idea that you are not capable. It acts as proof of inadequacy.

This distinction is vital. You cannot simply be clumsy and expect to be popular. You must first demonstrate that you are good at what you do. The blunder acts as a softener for your competence, not a substitute for it.

Real-world examples: from Jennifer Lawrence to brands

We see the Pratfall Effect in action in celebrity culture and marketing.

The relatable celebrity

Jennifer Lawrence is often cited as a modern example. Her frequent trips on the red carpet or candid, unpolished interviews often endear her to the public. Because she is an Oscar-winning, highly successful actress (high competence), these slips make her seem “down to earth” rather than clumsy.

The honest brand

Marketing experts use a similar concept known as the “blemishing effect.” When a brand admits a small flaw, consumers often trust it more. For example, Guinness: the beer brand famously turned a negative – the long time it takes to pour a pint – into a legendary slogan: “Good things come to those who wait.”

Why perfectionism harms connection

The Pratfall Effect challenges the idea that we must hide our flaws to be accepted. In social situations, perfection creates distance. We often struggle to connect with someone who seems to have no weaknesses because we cannot relate to them. This relates to understanding conversational biases to become more likable, where showing genuine engagement often matters more than saying the perfect thing.

When a competent person slips up, it levels the playing field. It signals vulnerability. This vulnerability fosters trust and signals that the person is authentic, not a curated persona.

What you can do about it

You do not need to stage accidents or spill coffee on purpose. However, you can change how you react to your own errors.

  • Don’t hide every flaw: If you are good at your job, admitting a small error or a gap in knowledge can make you more approachable to your team.
  • Own your blunders: When you trip or misspeak, laugh it off. Trying to cover it up often looks worse than the mistake itself.
  • Build competence first: Remember that this effect relies on a foundation of skill. Focus on being capable and reliable first.
  • Accept imperfection in others: Just as your mistakes humanize you, seeing others stumble is a reminder that everyone is human. This perspective can help reduce judgment and social anxiety.

Sources & related information

Elliot Aronson – The Effect of a Pratfall on Increasing Interpersonal Attractiveness – 1966

The original study published in Psychonomic Science where Aronson and his colleagues demonstrated that a blunder increases the attractiveness of a superior person but decreases the attractiveness of a mediocre person.

The Guardian (ZenithOptimedia) – The Pratfall effect and why brands should flaunt their flaws – 2015

An analysis of how brands like Guinness and VW use the Pratfall Effect to build trust by admitting minor weaknesses, making their core claims more believable.

Journal of Consumer Research – The blemishing effect – 2012

Research showing that under certain processing conditions, a small amount of negative information can actually enhance the positive impression of a product.

Endmyopia claims to reverse nearsightedness naturally (but science remains skeptical)

Imagine never needing your glasses again. No surgery, no contacts, just… fixing your eyes yourself. That’s the big promise of Endmyopia, a popular online method created by Jake Steiner. He claims you can reverse nearsightedness (myopia) just by changing your habits.

It sounds awesome, right? But before you throw away your glasses, you need to know that most eye doctors and scientists say it’s not that simple. Here is the lowdown on what this method is, why people try it, and why the medical consensus says it probably won’t work like you think.

The big claim: “Your glasses are the problem”

Endmyopia is based on a simple idea: your eyes aren’t broken; they are just reacting to your environment.

It starts with a muscle cramp

The theory goes like this: when you spend hours staring at your phone or laptop, a focusing muscle inside your eye gets tired and cramps up. This is called pseudo-myopia. At first, your vision is only blurry because of this cramp.

Then your eye grows longer

The controversial part is what happens next. Steiner says that when you wear glasses to fix that blur, your eye physically grows longer to “adapt” to the lenses. A longer eyeball is what causes true nearsightedness. Basically, the method claims your glasses trap you in a cycle that makes your vision worse.

The “fix”: training your eyes

To reverse this, Endmyopia tells you to do two things:

  1. Use weaker glasses: Instead of your full prescription, you wear weaker glasses for close-up work (like homework or gaming) to stop the eye strain.
  2. Practice “Active Focus”: This is a mental trick. You look at something far away that is slightly blurry (like a street sign) and try hard to make it clear just by focusing. The idea is that this effort forces your eyeball to shrink back to its normal size.

What science says

Here is the problem: Eye doctors agree that once your eyeball grows too long, it usually stays that way. It’s like your height – once you grow tall, you don’t shrink back down just because you want to.

Your eyeballs aren’t like muscles

You can train a muscle to get bigger, but you can’t really train an eyeball to get shorter. While atropine drops or special contact lenses can slow down eye growth in kids, there is no scientific proof that you can reverse it significantly once it’s happened.

Wearing weak glasses might backfire

Trying to fix your eyes by wearing weaker glasses can actually make things worse. A famous study (Chung et al., 2002) showed that under-correcting vision (wearing glasses that are too weak) made kids’ eyes grow faster, not slower. Blurry vision seems to signal the eye to keep growing, which is the exact opposite of what you want.

Why do some people swear it works?

If science says it doesn’t work, why are there so many success stories online? Read on Reddit: I was able to effectively fully cure myopia with my own methodology of eye exercises and discussions about do eye exercise really work?.

Brain training vs. Eye shrinking

When you practice looking at blurry things, your brain gets smarter at guessing what it’s seeing. This is called blur adaptation. You might be able to read a sign further away, not because your eyes are fixed, but because your brain is better at decoding the fuzzy image. You are “seeing” better, but your nearsightedness hasn’t actually disappeared.

Is there any hope?

Interestingly, some new research on red light therapy shows that specific light treatments might slightly shorten the eye.

Is it worth trying?

Trying to fix your eyes this way takes a huge amount of time – years of daily practice. Walking around (or driving!) with blurry vision can be dangerous.

Sources & related information

Study: Weak glasses make eyes worse (2002)

A major study showed that giving kids weaker glasses actually made their nearsightedness get worse faster.

Experts: Can you reverse myopia?

Eye doctors explain that while eye spasms can be fixed, the actual shape of a nearsighted eye is permanent.

Endmyopia Website

The source of the “active focus” method and the theory that glasses are to blame.

Red Light Therapy Study (2022)

A study showing that a specific type of red light therapy could shrink the eye slightly, proving some change is possible.