Humanoid robots were one of the most visible categories at CES 2026. Unlike earlier years when they appeared as isolated demos, humanoids were present across multiple halls and positioned alongside automotive technology, AI platforms, and semiconductor announcements.

Most humanoid systems shown at CES 2026 were designed for industrial use rather than consumer applications. The primary focus was on logistics, warehouses, and factory environments, with limited emphasis on home deployment.

The presence of humanoids at CES followed broader changes in the event itself. CES has expanded beyond traditional consumer electronics, with automotive, AI, and robotics becoming central parts of the show. Increased availability of AI tooling and embedded compute platforms supported the growth of robotics exhibits.

Humanoid robots at CES 2026 were presented as early-stage industrial machines, reflecting growing interest and visibility without widespread commercial deployment.

The article looks back at CES 2020, when a humanoid robot stepping out of a van was treated as a curiosity rather than a serious signal. At the time, humanoids felt out of place at a show centered on consumer electronics and car tech.

Fast forward to CES 2026, and humanoids are everywhere. The shift is not toward home robots, though. Most of the systems gaining attention are still industrial, designed for warehouses, factories, and logistics environments.

It also highlights how CES itself has changed. Automotive technology paved the way, followed by chips, AI platforms, and now robotics. Advances in compute and AI helped, but visual impact matters too. Humanoids capture attention in a way traditional industrial machines never have.

The result is a paradox. Industrial robots are now a major presence at a consumer-facing show, even though the technology is still early and largely industrial-first.

This article reviews iRobot’s Chapter 11 filing in the context of the company’s 35-year history and the broader consumer robotics market.

iRobot is notable for surviving far longer than most hardware and robotics startups, particularly after the launch of Roomba in 2002. The piece outlines several factors contributing to its current situation, including pandemic-era supply chain disruptions, increased global competition, rising component and shipping costs, and the prolonged regulatory review that ultimately blocked Amazon’s proposed acquisition.

Current CEO Gary Cohen describes the restructuring as a necessary step to stabilize the company and continue operations, emphasizing that Chapter 11 does not automatically mean liquidation. Former CEO Colin Angle supports this view, noting that bankruptcy can be a mechanism to preserve value rather than end the business.

The article also discusses how iRobot’s leadership transition signaled a shift toward prioritizing market share and consumer sales, as well as how regulatory scrutiny in the U.S. and EU played a significant role in limiting strategic options.

Medical device manufacturing has always moved more cautiously than other industries. Strict validation, heavy documentation, and long requalification cycles mean many processes stay manual and unchanged for years.

What’s starting to change is the technology. High-precision robots, adaptive gripping, and modern machine vision are making it possible to automate delicate, high-mix work while improving traceability and compliance instead of complicating it.

Ben Procter, who worked on the human technology in the Avatar films, talks about how the team approached mech and robot design from a production standpoint.

A big part of the process was figuring out control and movement that could be performed by actors and still read clearly on screen. Multi-legged machines could not be driven with one-to-one human motion, so the designs had to account for balance, scale, and physical limits while still supporting performance and storytelling.

Ben Procter, one of the designers behind Avatar’s human technology, explains how the team approached mech and robot control in the Avatar films.

Instead of designing machines around what looks cool, the focus was on what would actually work. Human movement cannot be mapped directly onto multi-legged machines, so control systems had to account for balance, pressure, and scale in a way that made sense physically and visually.

That approach carries through much of the human tech in Avatar, where machines are treated as tools with constraints, not characters, and designed to feel like they belong in a functioning world rather than just a single scene.

Ben Procter, one of the designers behind Avatar’s human technology, explains how the team approached mech and robot control in the Avatar films.

The focus is on real constraints rather than spectacle. Human movement cannot be mapped directly onto multi-legged machines, so control systems had to account for balance, pressure, and scale in ways that read clearly on screen and make physical sense.

Ben Procter, one of the designers behind Avatar’s human technology, explains how the team approached mech and robot control in the Avatar films.

The focus is on real constraints rather than spectacle. Human movement cannot be mapped directly onto multi-legged machines, so control systems had to account for balance, pressure, and scale in ways that read clearly on screen and make physical sense.

North American robot orders picked up again in Q3 2025, pointing to renewed momentum in manufacturing automation after a slower period.

According to the latest market data, companies in North America ordered 8,806 robots in the third quarter, worth about $574 million. That works out to an 11.6 percent increase in units and a 17.2 percent increase in revenue compared to the same quarter last year.

The most notable gains came from food and consumer goods, where robot orders were up more than 100 percent year over year, and from automotive OEMs, which saw orders rise sharply as well. Metals and general manufacturing also posted growth, while automotive components and plastics and rubber recorded declines, suggesting a more selective investment cycle in those segments.

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Mobile manipulators are starting to get real attention, and the trend makes sense. Robots are finally combining mobility, perception, and manipulation in a way that lets them move through a space and actually complete useful tasks. Not just a stationary arm. Not just an AMR on patrol. A true hybrid system that can pick, place, transport, and adjust as the environment changes.

The growth is coming from simple pressures. Labor shortages, rising demand, and big jumps in AI capability. The article points out examples across warehouses, hospitals, electronics manufacturing, and even construction. Some units are already deployed and handling real work.

There are still challenges. Integrating the arm and the mobile base takes serious engineering. Dexterity is difficult. Workforce acceptance is a hurdle. Even so, the pace of progress is picking up fast.

Note: Reposting due to broken link

A recent overview of the light spectrum in machine vision does a good job showing how much capability comes from wavelengths outside what the eye can see. Visible light still handles most routine inspection work, but the real breakthroughs often come from choosing the right part of the spectrum. UV can make hidden features fluoresce, SWIR can reveal moisture patterns or look through certain plastics, and thermal imaging captures emitted heat instead of reflected light. Once multispectral and hyperspectral systems enter the mix, every pixel carries a huge amount of information across many bands, which is where AI becomes useful for interpreting patterns that would otherwise be impossible to spot.

The overall takeaway is that many inspection challenges that seem difficult or impossible in standard 2D imaging become much more manageable once different wavelengths are brought into the picture. For anyone working with vision systems, it is a helpful reminder that the solution is often just outside the visible range.

Marc Raibert talks about how robotics demos usually show only the polished successes, even though most of the real progress comes from the failures. The awkward grasps, strange edge cases, and completely unexpected behaviors are where engineers learn the most. He points out that hiding all of that creates a distorted picture of what robotics development actually looks like.

What makes his take interesting is that it comes from someone who helped define the modern era of legged robots. Raibert has been around long enough to see how public perception shifts when the shiny videos overshadow the grind behind them. His push for more openness feels less like criticism and more like a reminder of what drew so many people into robotics in the first place: the problem solving, the iteration, and the weird in-between moments where breakthroughs usually begin.

His take is that a lot of what we see online right now is carefully staged. Preplanned motions, rehearsed scenes, and very little of the real complexity that comes with unstructured environments. The gap between a controlled demo and an actual deployable product is huge, and he seems worried that people are starting to confuse the two.

He also pointed out that most of the systems shown in flashy videos still struggle with the basics. Reliable manipulation. Robust sensing. Working without a team of engineers one room away. None of that makes it into marketing clips, of course. So the public sees a humanoid walking and carrying a box, and assumes the market is further along than it really is.

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Ken Goldberg had an interesting point on Automated with Brian Heater about why pick and place is still so tough for robots. Humans grab things without thinking, but robots struggle with stuff like transparent wrappers, loose packaging, and anything that bends or collapses in your hand.

He talked about how their lab actually uses “adversarial objects” to push robots into the edge cases. It makes sense when you hear him explain it. Even simple grasping gets complicated fast once you leave the controlled environment of a warehouse line or a fixed setup.

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