Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

This single leg robot is designed to “form a foundation for future bipedal robot development,” but personally, I think it’s perfect as is.

[ KAIST Dynamic Robot Control and Design Lab ]

Selling 17k social robots still amazes me. Aldebaran will be missed.

[ Aldebaran ]

Nice to see some actual challenging shoves as part of biped testing.

[ Under Control Robotics ]

[ Ground Control Robotics ]

Thanks, Dan!

It’s a nifty bootup screen, I’ll give them that.

[ Humanoid ]

Thanks, Kristina!

[ LocoTouch paper ]

Thanks, Changyi!

Not bad, although it’s worth pointing out that those shelves are not representative of any market I’ve ever been to.

[ Agility Robotics ]

It’s always cool to see robots presented as an incidental solution to a problem as opposed to, you know, robots.

The question that you really want answered, though, is “why is there water on the floor?”

[ Boston Dynamics ]

[ Disney Research ]

It’s been a week since ICRA 2025, and TRON 1 already misses all the new friends he made!

[ LimX Dynamics ]

ROB 450 in Winter 2025 challenged students to synthesize the knowledge acquired through their Robotics undergraduate courses at the University of Michigan to use a systematic and iterative design and analysis process and apply it to solving a real open-ended Robotics problem.

[ University of Michigan Robotics ]

What’s The Trick? A talk on human vs current robot learning, given by Chris Atkeson at the Robotics and AI Institute.

[ Robotics and AI Institute (RAI) ]

Take a look around the airport during your travels this summer and you might spot a string of new technologies at every touchpoint: from pre-arrival, bag drop, and security to the moment you board the plane.

In this new world, your face is your boarding pass, your electronic luggage tag transforms itself for each new flight, and gate scanners catch line cutters trying to sneak onto the plane early.

It isn’t the future—it’s now. Each of the technologies to follow is in use at airports around the world today, transforming your journey-before-the-journey.

As you pack the night before your trip, you ponder the age-old travel question: What time should I get to the airport? The right answer requires predicting the length of the security line. But at some airports, you no longer have to guess; in fact, you don’t have to wait in line at all.

Instead, you can book ahead and choose a specific time for your security screening—so you can arrive right before your reserved slot, confident that you’ll be whisked to the front of the line, thanks to Copenhagen Optimization’s Virtual Queuing system.

Copenhagen Optimization’s machine learning models use linear regression, heuristic models, and other techniques to forecast the volume of passenger arrivals based on historical data. The system is integrated with airport programs to access flight schedules and passenger-flow data from boarding-pass scans, and it also takes in data from lidar sensors and cameras at security checkpoints, X-ray luggage scanners, and other areas.

If a given day’s passenger volume ends up differing from historical projections, the platform can use real-time data from these inputs to adjust the Virtual Queuing time slots—and recommend that the airport make changes to security staffing and the number of open lanes. The Virtual Queuing system is constantly adjusting to flatten the passenger arrival curve, tactically redistributing demand across time slots to optimize resources and reduce congestion.

While this system is doing the most, you as a passenger can do the least. Just book a time slot on your airport’s website or app, and get some extra sleep knowing you’ll waltz right up to the security check tomorrow morning.

MCKIBILLO

Checking a bag? Here’s another step you can take care of before you arrive: Skip the old-school paper tags and generate your own electronic Bagtag. This e-ink device (costing about US $80, or €70) looks like a traditional luggage-tag holder, but it can generate a new, paperless tag for each one of your flights.

You provide your booking details through your airline’s app or the Bagtag app, and the Bagtag system then uses application programming interfaces and secure data protocols to retrieve the necessary information from the airline’s system: your name, flight details, the baggage you’re allowed, and the unique barcode that identifies your bag. The app uses this data to generate a digital tag. Hold your phone near your Bagtag, and it will transmit the encrypted tag data via Bluetooth or NFC. Simultaneously, your phone’s NFC antenna powers the battery-free Bagtag device.

On the Bagtag itself, a low-power microcontroller decrypts the tag data and displays the digital tag on the e-ink screen. Once you’re at the airport, the tag can be scanned at the airline’s self-service bag drop or desk, just like a traditional paper tag. The device also contains an RFID chip that’s compatible with the luggage-tracking systems that some airlines are using, allowing your bag to be identified and tracked—even if it takes a different journey than you do. When you arrive at the airport, just drop that checked bag and make your way to the security area.

MCKIBILLO

Over at security, you’ll need your boarding pass and ID. Compared with the old days of printing a physical slip from a kiosk, digital QR code boarding passes are quite handy—but what if you didn’t need anything besides your face? That’s the premise of Idemia Public Security’s biometric boarding-pass technology.

Instead of waiting in a queue for a security agent, you’ll approach a self-service kiosk or check-in point and insert your government-issued identification document, such as a driver’s license or passport. The system uses visible light, infrared, and ultraviolet imaging to analyze the document’s embedded security features and verify its authenticity. Then, computer-vision algorithms locate and extract the image of your face on the ID for identity verification.

Next, it’s time for your close-up. High-resolution cameras within the system capture a live image of your face using 3D and infrared imaging. The system’s antispoofing technology prevents people from trying to trick the system with items like photos, videos, or masks. The technology compares your live image to the one extracted from your ID using facial-recognition algorithms. Each image is then converted into a compact biometric template—a mathematical representation of your facial features—and a similarity score is generated to confirm a match.

Finally, the system checks your travel information against secure flight databases to make sure the ticket is valid and that you’re authorized to fly that day. Assuming all checks out, you’re cleared to head to the body scanners—with no biometric data retained by Idemia Public Security’s system.

MCKIBILLO

While you pass through your security screening, that luggage you checked is undergoing its own screening—with a major new upgrade that can tell exactly what’s inside.

Traditional scanners use one or a few X-ray sources and work by transmission, measuring the attenuation of the beam as it passes through the bag. These systems create a 2D “shadow” image based on differences in the amount and type of the materials inside. More recently, these systems have begun using computed tomography to scan the bag from all directions and to reconstruct 3D images of the objects inside. But even with CT, harmless objects may look similar to dangerous materials—which can lead to false positives and also require security staff to visually inspect the X-ray images or even bust open your luggage.

By contrast, Smiths Detection’s new X-ray diffraction machines measure the molecular structure of the items inside your bag to identify the exact materials—no human review required.

The machine uses a multifocus X-ray tube to quickly scan a bag from various angles, measuring the way the radiation diffracts while switching the position of the focal spots every few microseconds. Then, it analyzes the diffraction patterns to determine the crystal structure and molecular composition of the objects inside the bag—building a “fingerprint” of each material that can much more finely differentiate threats, like explosives and drugs, from benign items.

The system’s algorithms process this diffraction data and build a 3D spatial image, which allows real-time automated screening without the need for manual visual inspection by a human. After your bag passes through the X-ray diffraction machine without incident, it’s loaded into the cargo hold. Meanwhile, you’ve passed through your own scan at security and are ready to head toward your gate.

MCKIBILLO

While meandering over to your gate from security, you decide you could use a little pick-me-up. Just down the corridor is a convenience store with snacks, drinks, and other treats—but no cashiers. It’s a contactless shop that uses Just Walk Out technology by Amazon.

As you enter the store with the tap of a credit card or mobile wallet, a scanner reads the card and assigns you a unique session identifier that will let the Just Walk Out system link your actions in the store to your payment. Overhead cameras track you by the top of your head, not your face, as you move through the store.

The Just Walk Out system uses a deep-learning model to follow your movements and detect when you interact with items. In most cases, computer vision can identify a product you pick up simply based on the video feed, but sometimes weight sensors embedded in the shelves provide additional data to determine what you removed. The video and weight data are encoded as tokens, and a neural network processes those tokens in a way similar to how large language models encode text—determining the result of your actions to create a “virtual cart.”

While you shop, the system continuously updates this cart: adding a can of soda when you pick it up, swapping one brand of gum for another if you change your mind, or removing that bag of chips if you put it back on the shelf. Once your shopping is complete, you can indeed just walk out with your soda and gum. The items you take will make up your finalized virtual cart, and the credit card you entered the store with will be charged as usual. (You can look up a receipt, if you want.) With provisions procured, it’s onward to the gate.

MCKIBILLO

As you amble toward the gate with your luggage and snacks, you promptly spill that soda you just bought. Cleanup in Terminal C! Along comes Avidbots’ Neo, a fully autonomous floor-scrubbing robot designed to clean commercial spaces like airports with minimal human intervention.

When a Neo is first delivered to the airport, the robot performs a comprehensive scan of the various areas it will be cleaning using lidar and 3D depth cameras. Avidbots software processes the data to create a detailed map of the environment, including walls and other obstacles, and this serves as the foundation for Neo’s cleaning plans and navigation.

Neo’s human overlords can use a touchscreen on the robot to direct it to the area that needs cleaning—either as part of scheduled upkeep, or when someone (ahem) spills their soda. The robot springs into action, and as it moves, it continuously locates itself within its map and plans its movements using data from wheel encoders, inertial measurement units, and a gyroscope. Neo also updates its map and adjusts its path in real time by using the lidar and depth cameras to detect any changes from its initial mapping, such as a translocated trash can or perambulating passengers.

Then comes the scrubbing. Neo’s software plans the optimal path for cleaning a given area at this moment in time, adjusting the robot’s speed and steering as it moves along. A water-delivery system pumps and controls the flow of cleaning solution to the motorized brushes, whose speed and pressure can also be adjusted based on the surface the robot is cleaning. A powerful vacuum system collects the dirty water, and a flexible squeegee prevents slippery floors from being left behind.

While the robot’s various sensors and planning algorithms continuously detect and avoid obstacles, any physical contact with the robot’s bumpers triggers an emergency stop. And if Neo finds itself in a situation it’s just not sure how to handle, the robot will stop and call for assistance from a human operator, who can review sensor data and camera feeds remotely to help it along.

MCKIBILLO

Your airport journey is coming to an end, and your real journey is about to begin. As you wait at the gate, you notice a fair number of your fellow passengers hovering to board even before the agent has made any announcements. And when boarding does begin, a surprising number of people hop in line. Could all these people really be in boarding groups 1 and 2? you wonder.

If they’re not…they’ll get called out. American Airlines’ new boarding technology stops those pesky passengers who try to join the wrong boarding group and sneak onto the plane early.

If one such passenger approaches the gate before their assigned group has been called, scanning their boarding pass will trigger an audible alert—notifying the airline crew, and everyone else for that matter. The passengers will be politely asked to wait to board. As they slink back into line, try not to look too smug. After all, it’s been a remarkably easy, tech-assisted journey through the airport today.

The robots that share our public spaces today are so demure. Social robots and service robots aim to avoid offense, erring toward polite airs, positive emotions, and obedience. In some ways, this makes sense—would you really want to have a yelling match with a delivery robot in a hotel? Probably not, even if you’re in New York City and trying to absorb the local culture.

In other ways, this passive social robot design aligns with paternalistic standards that link assistance to subservience. Thoughtlessly following such outdated social norms in robot design may be ill-advised, since it can help to reinforce outdated or harmful ideas such as restricting people’s rights and reflecting only the needs of majority-identity users.

In my robotics lab at Oregon State University, we work with a playful spirit and enjoy challenging the problematic norms that are entrenched within “polite” interactions and social roles. So we decided to experiment with robots that use foul language around humans. After all, many people are using foul language more than ever in 2025. Why not let robots have a chance, too?

Societal standards in the United States suggest that cursing robots would likely rub people the wrong way in most contexts, as swearing has a predominantly negative connotation. Although some past research shows that cursing can enhance team cohesion and elicit humor, certain members of society (such as women) are often expected to avoid risking offense through profanity. We wondered whether cursing robots would be viewed negatively, or if they might perhaps offer benefits in certain situations.

We decided to study cursing robots in the context of responding to mistakes. Past work in human-robot interaction has already shown that responding to error (rather than ignoring it) can help robots be perceived more positively in human-populated spaces, especially in the case of personal and service robots. And one study found that compared to other faux pas, foul language is more forgivable in a robot.

With this past work in mind, we generated videos with three common types of robot failure: bumping into a table, dropping an object, and failing to grasp an object. We crossed these situations with three types of responses from the robot: no verbal reaction, a non-expletive verbal declaration, and an expletive verbal declaration. We then asked people to rate the robots on things like competence, discomfort, and likability, using standard scales in an online survey.

What If Robots Cursed? These Videos Helped Us Learn How People Feel about Profane RobotsVideo: Naomi Fitter

On the whole, we were surprised by how acceptable swearing seemed to the study participants, especially within an initial group of Oregon State University students, but even among the general public as well. Cursing had no negative impact, and even some positive impacts, among the college students after we removed one religiously connotated curse (god***it), which seemed to be received in a stronger negative way than other cuss words.

In fact, university participants rated swearing robots as the most socially close and most humorous, and rated non-expletive and expletive robot reactions equivalent on social warmth, competence, discomfort, anthropomorphism, and likability scales. The general public judged non-profane and profane robots as equivalent on most scales, although expletive reactions were deemed most discomforting and non-expletive responses seemed most likable. We believe that the university students were slightly more accepting of cursing robots because of the campus’s progressive culture, where cursing is considered a peccadillo.

Related: What’s the Deal With Robot Comedy?

Since experiments run solely in an online setting do not always represent real-life interactions well, we also conducted a final replication study in person with a robot that made errors while distributing goodie bags to campus community members at Oregon State, which reinforced our prior results.

Humans React to a Cursing Robot in the WildVideo: Naomi Fitter

We have submitted this work, which represents a well-designed series of empirical experiments with interesting results and replications along the way, to several different journals and conferences. Despite consistently enthusiastic reviewer comments, no editors have yet accepted our work for publication—it seems to be the type of paper that editors are nervous to touch. Currently, the work is under review for a fourth time, for possible inclusion in the 2025 IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), in a paper titled “Oh F**k! How Do People Feel About Robots That Leverage Profanity?”

Based on our results, we think cursing robots deserve a chance! Our findings show that swearing robots would typically have little downside and some upside, especially in open-minded spaces such as university campuses. Even for the general public, reactions to errors with profanity yielded much less distaste than we expected. Our data showed that people cared more about whether robots acknowledged their error at all than whether or not they swore.

People do have some reservations about cursing robots, especially when it comes to comfort and likability, so thoughtfulness may be required to apply curse words at the right time. For example, just as humans do, robots should likely hold back their swear words around children and be more careful in settings that typically demand cleaner language. Robot practitioners might also consider surveying individual users about profanity acceptance as they set up new technology in personal settings—rather than letting robotic systems learn the hard way, perhaps alienating users in the process.

As more robots enter our day-to-day spaces, they are bound to make mistakes. How they react to these errors is important. Fundamentally, our work shows that people prefer robots that notice when a mistake has occurred and react to this error in a relatable way. And it seems that a range of styles in the response itself, from the profane to the mundane, can work well. So we invite designers to give cursing robots a chance!

The robots that share our public spaces today are so demure. Social robots and service robots aim to avoid offense, erring toward polite airs, positive emotions, and obedience. In some ways, this makes sense—would you really want to have a yelling match with a delivery robot in a hotel? Probably not, even if you’re in New York City and trying to absorb the local culture.

In other ways, this passive social robot design aligns with paternalistic standards that link assistance to subservience. Thoughtlessly following such outdated social norms in robot design may be ill-advised, since it can help to reinforce outdated or harmful ideas such as restricting people’s rights and reflecting only the needs of majority-identity users.

In my robotics lab at Oregon State University, we work with a playful spirit and enjoy challenging the problematic norms that are entrenched within “polite” interactions and social roles. So we decided to experiment with robots that use foul language around humans. After all, many people are using foul language more than ever in 2025. Why not let robots have a chance, too?

Societal standards in the United States suggest that cursing robots would likely rub people the wrong way in most contexts, as swearing has a predominantly negative connotation. Although some past research shows that cursing can enhance team cohesion and elicit humor, certain members of society (such as women) are often expected to avoid risking offense through profanity. We wondered whether cursing robots would be viewed negatively, or if they might perhaps offer benefits in certain situations.

We decided to study cursing robots in the context of responding to mistakes. Past work in human-robot interaction has already shown that responding to error (rather than ignoring it) can help robots be perceived more positively in human-populated spaces, especially in the case of personal and service robots. And one study found that compared to other faux pas, foul language is more forgivable in a robot.

With this past work in mind, we generated videos with three common types of robot failure: bumping into a table, dropping an object, and failing to grasp an object. We crossed these situations with three types of responses from the robot: no verbal reaction, a non-expletive verbal declaration, and an expletive verbal declaration. We then asked people to rate the robots on things like competence, discomfort, and likability, using standard scales in an online survey.

On the whole, we were surprised by how acceptable swearing seemed to the study participants, especially within an initial group of Oregon State University students, but even among the general public as well. Cursing had no negative impact, and even some positive impacts, among the college students after we removed one religiously-connotated curse (god***it), which seemed to be received in a stronger negative way than other cuss words.

In fact, university participants rated swearing robots as the most socially close and most humorous, and rated non-expletive and expletive robot reactions equivalent on social warmth, competence, discomfort, anthropomorphism, and likability scales. The general public judged non-profane and profane robots as equivalent on most scales, although expletive reactions were deemed most discomforting and non-expletive responses seemed most likable. We believe that the university students were slightly more accepting of cursing robots because of the campus’s progressive culture, where cursing is considered a peccadillo.

Since experiments run solely in an online setting do not always represent real life interactions well, we also conducted a final replication study in person with a robot that made errors while distributing goodie bags to campus community members at Oregon State, which reinforced our prior results.

We have submitted this work, which represents a well designed series of empirical experiments with interesting results and replications along the way, to several different journals and conferences. Despite consistently enthusiastic reviewer comments, no editors have yet accepted our work for publication—it seems to be the type of paper that editors are nervous to touch. Currently, the work is under review for a fourth time, for possible inclusion in the 2025 IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), in a paper entitled “Oh F**k! How Do People Feel about Robots that Leverage Profanity?”

Based on our results, we think cursing robots deserve a chance! Our findings show that swearing robots would typically have little downside and some upside, especially in open-minded spaces such as university campuses. Even for the general public, reactions to errors with profanity yielded much less distaste than we expected. Our data showed that people cared more about whether robots acknowledged their error at all than whether or not they swore.

People do have some reservations about cursing robots, especially when it comes to comfort and likability, so thoughtfulness may be required to apply curse words at the right time. For example, just as humans do, robots should likely hold back their swear words around children and be more careful in settings that typically demand cleaner language. Robot practitioners might also consider surveying individual users about profanity acceptance as they set up new technology in personal settings—rather than letting robotic systems learn the hard way, perhaps alienating users in the process.

As more robots enter our day-to-day spaces, they are bound to make mistakes. How they react to these errors is important. Fundamentally, our work shows that people prefer robots that notice when a mistake has occurred and react to this error in a relatable way. And it seems that a range of styles in the response itself, from the profane to the mundane, can work well. So we invite designers to give cursing robots a chance!

As a mere earthling, I remember watching in fascination as Sojourner sent back photos of the Martian surface during the summer of 1997. I was not alone. The servers at NASA’s Jet Propulsion Lab slowed to a crawl when they got more than 47 million hits (a record number!) from people attempting to download those early images of the Red Planet. To be fair, it was the late 1990s, the Internet was still young, and most people were using dial-up modems. By the end of the 83-day mission, Sojourner had sent back 550 photos and performed more than 15 chemical analyses of Martian rocks and soil.

Sojourner, of course, remains on Mars. Pictured here is Marie Curie, its twin. Functionally identical, either one of the rovers could have made the voyage to Mars, but one of them was bound to become the famous face of the mission, while the other was destined to be left behind in obscurity. Did I write this piece because I feel a little bad for Marie Curie? Maybe. But it also gave me a chance to revisit this pioneering Mars mission, which established that robots could effectively explore the surface of planets and captivate the public imagination.

On 4 July 1997, the Mars Pathfinder parachuted through the Martian atmosphere and bounced about 15 times on glorified airbags before finally coming to a rest. The lander, renamed the Carl Sagan Memorial Station, carried precious cargo stowed inside. The next day, after the airbags retracted, the solar-powered Sojourner eased its way down the ramp, the first human-made vehicle to roll around on the surface of another planet. (It wasn’t the first extraterrestrial body, though. The Soviet Lunokhod rovers conducted two successful missions on the moon in 1970 and 1973. The Soviets had also landed a rover on Mars back in 1971, but communication was lost before the PROP-M ever deployed.)

This giant sandbox at JPL provided Marie Curie with an approximation of Martian terrain. Mike Nelson/AFP/Getty Images

The six-wheeled, 10.6-kilogram, microwave-oven-size Sojourner was equipped with three low-resolution cameras (two on the front for black-and-white images and a color camera on the rear), a laser hazard–avoidance system, an alpha-proton X-ray spectrometer, experiments for testing wheel abrasion and material adherence, and several accelerometers. The robot also demonstrated the value of the six-wheeled “rocker-bogie” suspension system that became NASA’s go-to design for all later Mars rovers. Sojourner never roamed more than about 12 meters from the lander due to the limited range of its radio.

Pathfinder had landed in Ares Vallis, an assumed ancient floodplain chosen because of the wide variety of rocks present. Scientists hoped to confirm the past existence of water on the surface of Mars. Sojourner did discover rounded pebbles that suggested running water, and later missions confirmed it.

A highlight of Sojourner’s 83-day mission on Mars was its encounter with a rock nicknamed Barnacle Bill [to the rover’s left]. JPL/NASA

As its first act of exploration, Sojourner rolled forward 36 centimeters and encountered a rock, dubbed Barnacle Bill due to its rough surface. The rover spent about 10 hours analyzing the rock, using its spectrometer to determine the elemental composition. Over the next few weeks, while the lander collected atmospheric information and took photos, the rover studied rocks in detail and tested the Martian soil.

Meanwhile back on Earth, engineers at JPL used Marie Curie to mimic Sojourner’s movements in a Mars-like setting. During the original design and testing of the rovers, the team had set up giant sandboxes, each holding thousands of kilograms of playground sand, in the Space Flight Operations Facility at JPL. They exhaustively practiced the remote operation of Sojourner, including an 11-minute delay in communications between Mars and Earth. (The actual delay can vary from 7 to 20 minutes.) Even after Sojourner landed, Marie Curie continued to help them strategize.

Initially, Sojourner was remotely operated from Earth, which was tricky given the lengthy communication delay. Mike Nelson/AFP/Getty Images

During its first few days on Mars, Sojourner was maneuvered by an Earth-based operator wearing 3D goggles and using a funky input device called a Spaceball 2003. Images pieced together from both the lander and the rover guided the operator. It was like a very, very slow video game—the rover sometimes moved only a few centimeters a day. NASA then turned on Sojourner’s hazard-avoidance system, which allowed the rover some autonomy to explore its world. A human would suggest a path for that day’s exploration, and then the rover had to autonomously avoid any obstacles in its way, such as a big rock, a cliff, or a steep slope.

JPL designed Sojourner to operate for a week. But the little rover that could kept chugging along for 83 Martian days before NASA finally lost contact, on 7 October 1997. The lander had conked out on 27 September. In all, the mission collected 1.2 gigabytes of data (which at the time was a lot) and sent back 10,000 images of the planet’s surface.

NASA held on to Marie Curie with the hopes of sending it on another mission to Mars. For a while, it was slated to be part of the Mars 2001 set of missions, but that didn’t happen. In 2015, JPL transferred the rover to the Smithsonian’s National Air and Space Museum.

The Pathfinder mission was the second one in NASA administrator Daniel S. Goldin’s Discovery Program, which embodied his “faster, better, cheaper” philosophy of making NASA more nimble and efficient. (The first Discovery mission was to the asteroid Eros.) In the financial climate of the early 1990s, the space agency couldn’t risk a billion-dollar loss if a major mission failed. Goldin opted for smaller projects; the Pathfinder mission’s overall budget, including flight and operations, was capped at US $300 million.

RELATED: How NASA Built Its Mars Rovers

In his 2014 book Curiosity: An Inside Look at the Mars Rover Mission and the People Who Made It Happen (Prometheus), science writer Rod Pyle interviews Rob Manning, chief engineer for the Pathfinder mission and subsequent Mars rovers. Manning recalled that one of the best things about the mission was its relatively minimal requirements. The team was responsible for landing on Mars, delivering the rover, and transmitting images—technically challenging, to be sure, but beyond that the team had no constraints.

Sojourner was succeeded by the rovers Spirit, Opportunity, and Curiosity. Shown here are four mission spares, including Marie Curie [foreground]. JPL-Caltech/NASA

The real mission was to prove to Congress and the American public that NASA could do groundbreaking work more efficiently. Behind the scenes, there was a little bit of accounting magic happening, with the “faster, better, cheaper” missions often being silently underwritten by larger, older projects. For example, the radioisotope heater units that kept Sojourner’s electronics warm enough to operate were leftover spares from the Galileo mission to Jupiter, so they were “free.”

Not only was the Pathfinder mission successful but it captured the hearts of Americans and reinvigorated an interest in exploring Mars. In the process, it set the foundation for the future missions that allowed the rovers Spirit, Opportunity, and Curiosity (which, incredibly, is still operating nearly 13 years after it landed) to explore even more of the Red Planet.

To name its first Mars rovers, NASA launched a student contest in March 1994, with the specific guidance of choosing a “heroine.” Entry essays were judged on their quality and creativity, the appropriateness of the name for a rover, and the student’s knowledge of the woman to be honored as well as the mission’s goals. Students from all over the world entered.

Twelve-year-old Valerie Ambroise of Bridgeport, Conn., won for her essay on Sojourner Truth, while 18-year-old Deepti Rohatgi of Rockville, Md., came in second for hers on Marie Curie. Truth was a Black woman born into slavery at the end of the 18th century. She escaped with her infant daughter and two years later won freedom for her son through legal action. She became a vocal advocate for civil rights, women’s rights, and alcohol temperance. Curie was a Polish-French physicist and chemist famous for her studies of radioactivity, a term she coined. She was the first woman to win a Nobel Prize, as well as the first person to win a second Nobel.

NASA subsequently recognized several other women with named structures. One of the last women to be so honored was Nancy Grace Roman, the space agency’s first chief of astronomy. In May 2020, NASA announced it would name the Wide Field Infrared Survey Telescope after Roman; the space telescope is set to launch as early as October 2026, although the Trump administration has repeatedly said it wants to cancel the project.

Related: A Trillion Rogue Planets and Not One Sun to Shine on Them

These days, NASA tries to avoid naming its major projects after people. It quietly changed its naming policy in December 2022 after allegations came to light that James Webb, for whom the James Webb Space Telescope is named, had fired LGBTQ+ employees at NASA and, before that, the State Department. A NASA investigation couldn’t substantiate the allegations, and so the telescope retained Webb’s name. But the bar is now much higher for NASA projects to memorialize anyone, deserving or otherwise. (The agency did allow the hopping lunar robot IM-2 Micro Nova Hopper, built by Intuitive Machines, to be named for computer-software pioneer Grace Hopper.)

And so Marie Curie and Sojourner will remain part of a rarefied clique. Sojourner, inducted into the Robot Hall of Fame in 2003, will always be the celebrity of the pair. And Marie Curie will always remain on the sidelines. But think about it this way: Marie Curie is now on exhibit at one of the most popular museums in the world, where millions of visitors can see the rover up close. That’s not too shabby a legacy either.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the June 2025 print issue.

Curator Matthew Shindell of the National Air and Space Museum first suggested I feature Marie Curie. I found additional information from the museum’s collections website, an article by David Kindy in Smithsonian magazine, and the book After Sputnik: 50 Years of the Space Age (Smithsonian Books/HarperCollins, 2007) by Smithsonian curator Martin Collins.

NASA has numerous resources documenting the Mars Pathfinder mission, such as the mission website, fact sheet, and many lovely photos (including some of Barnacle Bill and a composite of Marie Curie during a prelaunch test).

Curiosity: An Inside Look at the Mars Rover Mission and the People Who Made It Happen (Prometheus, 2014) by Rod Pyle and Roving Mars: Spirit, Opportunity, and the Exploration of the Red Planet (Hyperion, 2005) by planetary scientist Steve Squyres are both about later Mars missions and their rovers, but they include foundational information about Sojourner.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

[Boston Dynamics]

[LimX Dynamics]

Thanks, Jinyan!

[University of Massachusetts Amherst]

Thanks, Julia!

[RIVR]

We will have more on this robot shortly, but for now, this is all you need to know.

[Pintobotics]

Some pretty awesome quadruped parkour here—haven’t seen the wall running before.

[Paper] via [Science Robotics]

This is fun, and also useful, because it’s all about recovering from unpredictable and forceful impacts.

What is that move at 0:06, though?! Wow.

[Unitree]

Maybe an option for all of those social robots that are now not social?

[RoboHearts]

Oh, good, another robot I want nowhere near me.

[SDU Biorobotics Lab, University of Southern Denmark]

While this “has become the first humanoid robot to skillfully use chopsticks,” I’m pretty skeptical of the implied autonomy. Also, those chopsticks are cheaters.

[ROBOTERA]

Looks like Westwood Robotics had a fun time at ICRA!

[Westwood Robotics]

[Dusty Robotics]

As drones evolve into critical agents across defense, disaster response, and infrastructure inspection, they must become more adaptive, secure, and resilient. Traditional AI methods fall short in real-world unpredictability. This whitepaper from the Technology Innovation Institute (TII) explores how Embodied AI – AI that integrates perception, action, memory, and learning in dynamic environments, can revolutionize drone operations. Drawing from innovations in GenAI, Physical AI, and zero-trust frameworks, TII outlines a future where drones can perceive threats, adapt to change, and collaborate safely in real time. The result: smarter, safer, and more secure autonomous aerial systems.

Download this free whitepaper now!

Less than three years ago, these were bare fields in humble Ellabell, Georgia. Today, the vast Hyundai Motor Group Metaplant is exactly what people imagine when they talk about the future of EV and automobile manufacturing in America.

I’ve driven the 2026 Hyundai Ioniq9 here from nearby Savannah, a striking three-row electric SUV with everything it takes to succeed in today’s market: up to 530 kilometers (335 miles) of efficient driving range, the latest features and tech, and a native NACS connector that lets owners—finally—hook into Tesla Superchargers with streamlined Plug and Charge ease.

The success of the Ioniq9 and popular Ioniq5 crossover is deeply intertwined with the US $7.6 billion Metaplant, whose inaugural 2025 Ioniq5 rolled off its assembly line in October. That includes the Ioniq models’ full eligibility for $7,500 consumer tax credits for U.S.-built EVs with North American batteries, although the credits are on the Trump administration’s chopping block. Still, the factory gives Hyundai a bulwark and some breathing room against potential tariffs and puts the South Korean automaker ahead of many rivals.

With 11 cavernous buildings and a massive 697,000 square meters (7.5 million square feet) of space, it’s set to become America’s largest dedicated plant for EVs and hybrids, with capacity for 500,000 Hyundai, Kia, and Genesis models per year. (Tesla’s Texas Gigafactory can produce 375,000.) Company executives say this is North America’s most heavily automated factory, bar none, a showcase for AI and robotic tech.

The factory is also environmentally friendly, as I see when I roll into the factory: “Meta Pros,” as Hyundai calls its workers, can park in nearly 1,900 spaces beneath solar roofs, shielded from the baking Georgia sun that provides up to 5 percent of the plant’s electricity. The automaker has a target of obtaining 100 percent of its energy from renewable sources. Those include hydrogen trucks from the Hyundai-owned Xcient, the world’s first commercialized hydrogen fuel-cell semis. A fleet of 21 trucks haul parts here from area suppliers, taking advantage of 400-kilometer driving ranges with zero tailpipe emissions. The bulk of finished vehicles are shipped by rail rather than truck, trimming fossil-fuel emissions and the automaker’s carbon footprint.

At the docks, some of the plant’s 850 robots unload parts from the hydrogen trucks. About 300 automated guided vehicles, or AGVs, glide around the factory with no tracks required, smartly avoiding human workers. As part of an AI-based procurement and logistics system, the AGVs automatically allocate and ferry parts to their proper work stations for just-in-time delivery, saving space, time, and money otherwise used to stockpile parts.

“They’re delivering the right parts to the right station at the right time, so you’re no longer relying on people to make decisions,” says Jerry Roach, senior manager of general assembly.

The building blocks of a modern unibody car chassis, called “bodies in white,” are welded by an army of 475 robots at Hyundai’s new plant.Hyundai

I’ve seen AGVs in action around the world, but the Metaplant shows me a new trick: A pair of sled-like AGVs slide below these electric Hyundais as they roll off the line. They grab and hoist their wheels and autonomously ferry the finished Hyundais to a parking area, with no need for a human driver.

Some companies have strict policies about pets at work. Here, Spots—robotic quadrupeds designed by Hyundai-owned Boston Dynamics—use 360-degree vision and “athletic intelligence” to sniff out potential defects on car welds. Those four-legged friends may soon have a biped partner: Atlas, the humanoid robots from Boston Dynamics whose breathtaking physical skills—including crawling, cartwheeling, and even breakdance moves—have observers wondering if autoworkers are next in line to be replaced by AI. Hyundai executives say that’s not the case, even as they plan to deploy Atlas models (non-union of course) throughout their global factories. With RGB cameras in their charming 360-degree swiveling heads, Atlas robots are being trained to sense their environments, avoid collisions, and manipulate and move parts in factories in impressively complex sequences.

The welding shop alone houses 475 industrial robots, among about 850 in total. I watch massive robots cobble together “bodies in white,” the building blocks of every car chassis, with ruthless speed and precision. A trip to the onsite steel stamping plant reveals a facility so quiet that no ear protection is required. Here, a whirling mass of robots stamp out roofs, fenders, and hoods, which are automatically stored in soaring racks overhead.

Roach says the Metaplant offered a unique opportunity to design an electrified car plant from the ground up, rather than retrofit an existing factory that made internal-combustion cars, which even Tesla and Rivian were forced to do in California and Illinois, respectively.

Regarding automation replacing human workers, Roach acknowledges that some of it is inevitable. But robots are also freeing humans from heavy lifting and repetitive, mindless tasks that, for decades, made factory work both hazardous and unfulfilling.

He offers a technical first as an example: A collaborative robot—sophisticated enough to work alongside humans with no physical separation for safety—installs bulky doors on the assembly line. It’s a notoriously cumbersome process to perform without scratching the pretty paint on a door or surrounding panels.

“Guess what? Robots do that perfectly,” Roach says. “They always put the door in the exact same place. So here, that technology makes sense.”

It also frees people to do what they’re best at: precision tasks that require dexterous fingers, vision, intelligence, and skill. “I want my people doing craftsmanship,” Roach says.

The plant currently employs 1,340 Meta Pros at an annual average pay of $58,100. That’s 25 percent higher than average in Bryan County, Ga. Hyundai’s annual local payroll has already reached $497 million. The company foresees an eventual 8,500 jobs on site and another 7,000 indirect jobs for local suppliers and businesses.

On the battery front, Hyundai is currently sourcing cells from Georgia and SK On, with some Ioniq5 batteries imported from Hungary. But the Metaplant campus includes the HL-GA battery company. The $4 billion plant, a joint operation with LG Energy Solutions, plans to produce nickel-cobalt-magnesium cells beginning next year, assembled into packs on site by Hyundai’s Mobis subsidiary. Hyundai is also on track to open a second $5 billion battery plant in Georgia, a joint operation with SK On. It’s all part of Hyundai’s planned $21 billion in U.S. investment between now and 2028—more than the $20 billion it invested since entering the U.S. market in 1986. Even a robot could crunch those numbers and come away impressed.

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

[DRAGON Lab]

Thanks, Moju!

[Designing Emergence Lab]

[CMU]

Spot is getting some AI upgrades to help it with industrial inspection.

[Boston Dynamics]

[UMich]

[Tool as Interface]

Thanks, Haonan!

[Universal Robots]

[RoMeLa]

[LimX Dynamics]

[RoboDesign Lab]

[TED]

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

[ DARPA ]

[ LimX Dynamics ]

Thanks, Jinyan!

[ DEEP Robotics ]

The sound in this video is either excellent or terrible, I’m not quite sure which.

[ TU Berlin ]

[ FALCON ]

[ Carnegie Mellon University ]

[ MIT ]

[ Sanctuary AI ]

[ Canvas ] via [ Universal Robots ]

[ Torc ]

This Stanford Robotics Center talk is by Jonathan Hurst at Agility Robotics, on “Humanoid Robots: From the Warehouse to Your House.”

[ Stanford ]

Being long and skinny and wiggly is a strategy that’s been wildly successful for animals, ever since there have been animals, more or less. Roboticists, eternally jealous of biology, have taken notice of this, and have spent decades trying to build robotic versions of snakes, salamanders, worms, and more. There’s been some success, of a sort, although most of the robotic snakes and whatnot that we’ve seen have been for things like disaster relief, which is kind of just what you do when you have a robot with a novel movement strategy but without any other obvious practical application.

Dan Goldman at Georgia Tech has been working on bioinspired robotic locomotion for as long as anyone, and as it turns out, that’s exactly the amount of time that it takes to develop a long and skinny and wiggly robot with a viable commercial use case. Goldman has a new Atlanta-based startup called Ground Control Robotics (GCR) that’s bringing what are essentially giant robotic arthropods to agricultural crop management.

- YouTube

I’m not entirely sure what you’d call this—a robotic giant centipede might be the easiest description to agree on, I guess? But Goldman tells us that he doesn’t consider his robots to be bioinspired as much as they’re “robophysical” models of living systems. “I like the idea of carefully studying the animals,” Goldman says. “We use the models to test biological principles, discover new phenomena with them, and then bring those insights into hardened robots which can go outside of the lab.”

The robot itself is not that complicated, at least on the scale of how complicated robots usually are. It’s made up of a head with some sensors in it plus a handful of identical cable-connected segments, each with a couple of motors for leg actuation. On paper, this works out to be a lot of degrees of freedom, but you can get surprisingly good performance using relatively simple control techniques.

“Centipede robots, like snake robots, are basically swimmers,” Goldman says. The key difference is that adding legs expands the different kinds of environments through which swimming robots can move. The right pattern of lifting and lowering the legs generates a fluidlike thrust force that helps the robot to push off more stuff as it moves to make its motion more consistent and reliable. “We created a new kind of mechanism to take actuation away from the centerline of the robot to the sides, using cables back and forth,” says Goldman. “When you tune things properly, the robot goes from being stiff to unidirectionally compliant. And if you do that, what you find is almost like magic—this thing swims through arbitrarily complex environments with no brain power.”

The complex environments that the robot is designed for are agricultural. Think sensing and weed control in fields, but don’t think about gentle rolling hills lined with neat rows of crops. That kind of farming is very amenable to automation at scale, and there are plenty of robotics companies in that space already. Not all plants grow in well-kept rows on mostly flat ground, however: Perennial crops, where the plant itself sticks around and you harvest stuff off of it every year, can be much more complicated to manage. This is especially true for crops like wine grapes, which can grow on very steep and often rocky slopes. Those kinds of environments are an opportunity for GCR’s robots, offering an initial use case that brings the robot from academic curiosity to something with unique commercial potential.

Wiggly antennae-like structures help the robot to climb over obstacles taller than itself.Ground Control Robotics

“Robotics researchers tend to treat robots as one-off demonstrations of a theory or principle,” Goldman says. “You get the darn thing to work, you submit it to [the International Conference on Robotics and Automation], and then you go onto the next thing. But we’ve had to build in robustness from the get-go, because our robots are experimental physics tools.” Much of the research that Goldman does in his lab is on using these robo-physical models to try to systematically test and (hopefully) understand how animals move the way that they do. “And that’s where we started to see that we could have these robots not just be laboratory toys,” says Goldman, “but that they could become a minimum viable product.”

According to GCR, there is currently no automated solution for weed control around scraggly bushy or vinelike plants (like blueberries or strawberries or grapes), and farmers can spend an enormous amount of money having humans crawl around under the plants to check health and pull weeds. GCR estimates that weed control for blueberries in California can run US $300 per acre or more, and strawberries are even worse, sometimes more than $1,000 per acre. It’s not a fun job, and it’s getting increasingly difficult to find humans willing to do it. For farmers who don’t want to spray pesticides, there aren’t a lot of good options, and GCR thinks that its robotic centipedes could fill that niche.

An obvious question with any novel robotic mobility system is whether you could accomplish basically the same thing with a system that’s much less novel. Like, quadrupeds are getting pretty good these days, why not just use one of them? Or a wheeled robot, for that matter? “We want to send the robot as close to the crops as possible,” says Goldman. “And we don’t want a bigger, clunkier machine to destroy those fields.” This gets back to the clutter problem: A robot large enough to ignore clutter could cause damage, and most robots small enough not to damage clutter become a nightmare of a control problem.

When most of the obstacles that robots encounter are at a comparable scale to themselves, control becomes very difficult. “The terrain reaction forces are almost impossible to predict,” explains Goldman, which means that the robot’s mobility regime gets dominated by environmental noise. One approach would be to try to model all of this noise and the resulting dynamics and implement some kind of control policy, but it turns out that there’s a much simpler strategy: more legs. “It’s possible to generate reliable motion without any sensing at all,” says Goldman, “if we have a lot of legs.”

For this design of robot, adding more legs is easy, which is another advantage of this type of mobility over something like a quadruped. Each of GCR’s robots will cost a lot less than you probably think—likely in the thousand-dollar range, because the leg modules themselves are relatively cheap, and most of the intelligence is mechanical rather than sense-based or compute-based. The concept is that a decentralized swarm of these robots would operate in fields 24/7—just scouting for now, where there’s still a substantial amount of value, and then eventually physically ripping out weeds with some big robotic centipede jaws (or maybe even lasers!) for a lower cost than any other option.

Eventually, these robots will operate autonomously in swarms, and could also be useful for applications like disaster response.Ground Control Robotics

Ground Control Robotics is currently working with a blueberry farmer and a vineyard owner in Georgia on pilot projects to refine the mobility and sensing capabilities of the robots within the next few months. Obviously, there are options to expand into disaster relief (for real) and perhaps even military applications, although Goldman tells us that different environments might require different limb configurations or the ability to tuck the limbs away entirely. I do appreciate that GCR is starting with an application that will likely take a lot more work but also a lot more potential. It’s not often that we get to see such a direct transition between novel robotics research and a commercial product, and while it’s certainly going to be a challenge, I’ve already put my backyard garden on the waiting list.

The main assumption about humanoid robotics that the industry is making right now is that the most realistic near-term pathway to actually making money is in either warehouses or factories. It’s easy to see where this assumption comes from: Repetitive tasks requiring strength or flexibility in well-structured environments is one place where it really seems like robots could thrive, and if you need to make billions of dollars (because somehow that’s how much your company is valued at), it doesn’t appear as though there are a lot of other good options.

Cartwheel Robotics is trying to do something different with humanoids. Cartwheel is more interested in building robots that people can connect with, with the eventual goal of general-purpose home companionship. Founder Scott LaValley describes Cartwheel’s robot as “a small, friendly humanoid robot designed to bring joy, warmth, and a bit of everyday magic into the spaces we live in. It’s expressive, emotionally intelligent, and full of personality—not just a piece of technology but a presence you can feel.”

This rendering shows the design and scale of Cartwheel’s humanoid prototype.Cartwheel

Historically, making a commercially viable social robot is a huge challenge. A little less than a decade ago, a series of social home robots (backed by a substantial amount of investment) tried very, very hard to justify themselves to consumers and did not succeed. Whether the fundamental problems with the concept of social home robots (namely, cost and interactive novelty) have been solved at this point isn’t totally clear, but Cartwheel is making things even more difficult for themselves by going the humanoid route, legs and all. That means dealing with all kinds of problems from motion planning to balancing to safety, all in a way that’s reliable enough for the robot to operate around children.

LaValley is arguably one of the few people who could plausibly make a commercial social humanoid actually happen. His extensive background in humanoid robotics includes nearly a decade at Boston Dynamics working on the Atlas robots, followed by five years at Disney, where he led the team that developed Disney’s Baby Groot robot.

In humanoid robot terms, there’s quite a contrast between the versions of Atlas that LaValley worked on (DRC Atlas in particular) and Baby Groot. They’re obviously designed and built to do very different things, but LaValley says that what really struck him was how his kids reacted when he introduced them to the robots he was working on. “At Boston Dynamics, we were known for terrifying robots,” LaValley remembers. “I was excited to work on the Atlas robots because they were cool technology, but my kids would look at them and go, ‘That’s scary.’ At Disney, I brought my kids in and they would light up with a big smile on their face and ask, ‘Is that really Baby Groot? Can I give it a hug?’ And I thought, this is the type of experience I want to see robots delivering.” While Baby Groot was never a commercial project, for LaValley it marked a pivotal milestone in emotional robotics that shaped his vision for Cartwheel: “Seeing how my kids connected with Baby Groot reframed what robots could and should evoke.”

The current generation of commercial humanoids is pretty much the opposite of what LaValley is looking for. You could argue that this is because they’re designed to do work, rather than be anyone’s friend, but many of the design choices seem to be based on the sort of thing that would be the most eye-catching to the public (and investors) in a rather boringly “futuristic” way. And look, there are plenty of good reasons why you might want to very deliberately design a humanoid with commercial (or at least industrial) aspirations to look or not look a certain way, but for better or worse, nobody is going to like those robots. Respect them? Sure. Think they’re cool? Probably. Want to be friends with them? Not likely. And for Cartwheel, this is the opportunity, LaValley says. “These humanoid robots are built to be tools. They lack personality. They’re soulless. But we’re designing a robot to be a humanoid that humans will want in their day-to-day lives.”

Eventually, Cartwheel’s robots will likely need to be practical (as this rendering suggests) in order to find a place in people’s homes.Cartwheel

Yogi is one of Cartwheel’s prototypes, which LaValley describes as having “toddler proportions,” which are the key to making it appear friendly and approachable. “It has rounded lines, with a big head, and it’s even a little chubby. I don’t see a robot when I see Yogi; I see a character.” A second prototype, called Speedy, is a bit less complicated and is intended to be more of a near-term customizable commercial platform. Think something like Baby Groot, except available as any character you like, and to companies who aren’t Disney. LaValley tells us that a version of Speedy with a special torso designed for a “particular costume” is headed to a customer in the near future.

As the previous generation of social robots learned the hard way, it takes a lot more than good looks for a robot to connect with humans over the long term. Somewhat inevitably, LaValley sees AI as one potential answer to this, since it might offer a way of preserving novelty by keeping interactions fresh. This extends beyond verbal interactions, too, and Cartwheel is experimenting with using AI for whole-body motion generation, where each robot behavior will be unique, even under the same conditions or when given the same inputs.

While Cartwheel is starting with a commercial platform, the end goal is to put these small social humanoids into homes. This means considering safety and affordability in a way that doesn’t really apply to humanoids that are designed to work in warehouses or factories. The small size of Cartwheel’s robots will certainly help with both of those things, but we’re still talking about a robot that’s likely to cost a significant amount—certainly more than a major appliance, although perhaps not as much as a new car, is as much as LaValley was willing to commit to at this point. With that kind of price comes high expectations, and for most people, the only way to justify buying a home humanoid will be if it can somehow be practical as well as lovable.

LaValley is candid about the challenge here: “I don’t have all the answers,” he says. “There’s a lot to figure out.” One approach that’s becoming increasingly common with robots is to go with a service model, where the robot is essentially being rented in the same way that you might pay for the services of a housekeeper or gardener. But again, for that to make sense, Cartwheel’s robots will have to justify themselves financially. “This problem won’t be solved in the next year, or maybe not even in the next five years,” LaValley says. “There are a lot of things we don’t understand—this is going to take a while. We have to work our way to understanding and then addressing the problem set, and our approach is to find development partners and get our robots out into the real world.”

Cartwheel

Cartwheel has been in business for three years now, and got off the ground by providing robotics engineering services to corporate customers. That, along with an initial funding round, allowed LaValley to bootstrap the development of Cartwheel’s own robots, and he expects to deliver a couple dozen variations on Speedy to places like museums and science centers over the next 12 months.

The dream, though, is small home robots that are both companionable and capable, and LaValley is even willing to throw around terms like “general purpose.” “Capability increases over time,” he says, “and maybe our robots will be able to do more than just play with your kids or pick up a few items around the house. I see all robots eventually moving towards general purpose. Our strategy is not to get to general purpose on day one, or even get into the home day one. But we’re working towards that goal. That’s our north star.”

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

Today I learned that “hippotherapy” is not quite what I wanted it to be.

[ Kuka ]

[ MIT News ]

[ GitHub ] via [ Human Centered Autonomy Lab ]

Thanks, Haonan!

[ EPFL ]

[ Paper ] via [ Singapore University of Technology and Design ]

I like the idea of a humanoid that uses jumping as a primary locomotion mode not because it has to, but because it’s fun.

[ PAL Robotics ]

I had not realized how much nuance there is to digging stuff up with a shovel.

[ Intelligent Motion Laboratory ]

[ Michigan Robotics ]

If you have a humanoid robot and you’re wondering how it should communicate, here’s the answer.

[ Pollen ]

Whose side are you on, Dusty?

Even construction robots should be mindful about siding with the Empire, though there can be consequences!

- YouTube

[ Dusty Robotics ]

This Michigan Robotics Seminar is by Danfei Xu from Georgia Tech, on “Generative Task and Motion Planning.”

[ Michigan Robotics ]

At an event in Dortmund, Germany today, Amazon announced a new robotic system called Vulcan, which the company is calling “its first robotic system with a genuine sense of touch—designed to transform how robots interact with the physical world.” In the short to medium term, the physical world that Amazon is most concerned with is its warehouses, and Vulcan is designed to assist (or take over, depending on your perspective) with stowing and picking items in its mobile robotic inventory system.

In two upcoming papers in IEEE Transactions on Robotics, Amazon researchers describe how both the stowing and picking side of the system operates. We covered stowing in detail a couple of years ago, when we spoke with Aaron Parness, the director of applied science at Amazon Robotics. Parness and his team have made a lot of progress on stowing since then, improving speed and reliability over more than 500,000 stows in operational warehouses to the point where the average stowing robot is now slightly faster than the average stowing human. We spoke with Parness to get an update on stowing, as well as an in-depth look at how Vulcan handles picking, which you can find in this separate article. It’s a much different problem, and well worth a read.

Stowing is the process by which Amazon brings products into its warehouses and adds them to its inventory so that you can order them. Not surprisingly, Amazon has gone to extreme lengths to optimize this process to maximize efficiency in both space and time. Human stowers are presented with a mobile robotic pod full of fabric cubbies (bins) with elastic bands across the front of them to keep stuff from falling out. The human’s job is to find a promising space in a bin, pull the plastic band aside, and stuff the thing into that space. The item’s new home is recorded in Amazon’s system, the pod then drives back into the warehouse, and the next pod comes along, ready for the next item.

Different manipulation tools are used to interact with human-optimized bins.Amazon

The new paper on stowing includes some interesting numbers about Amazon’s inventory-handling process that helps put the scale of the problem in perspective. More than 14 billion items are stowed by hand every year at Amazon warehouses. Amazon is hoping that Vulcan robots will be able to stow 80 percent of these items at a rate of 300 items per hour, while operating 20 hours per day. It’s a very, very high bar.

After a lot of practice, Amazon’s robots are now quite good at the stowing task. Parness tells us that the stow system is operating three times as fast as it was 18 months ago, meaning that it’s actually a little bit faster than an average human. This is exciting, but as Parness explains, expert humans still put the robots to shame. “The fastest humans at this task are like Olympic athletes. They’re far faster than the robots, and they’re able to store items in pods at much higher densities.” High density is important because it means that more stuff can fit into warehouses that are physically closer to more people, which is especially relevant in urban areas where space is at a premium. The best humans can get very creative when it comes to this physical three-dimensional “Tetris-ing,” which the robots are still working on.

Where robots do excel is planning ahead, and this is likely why the average robot stower is now able to outpace the average human stower—Tetris-ing is a mental process, too. In the same way that good Tetris players are thinking about where the next piece is going to go, not just the current piece, robots are able to leverage a lot more information than humans can to optimize what gets stowed where and when, says Parness. “When you’re a person doing this task, you’ve got a buffer of 20 or 30 items, and you’re looking for an opportunity to fit those items into different bins, and having to remember which item might go into which space. But the robot knows all of the properties of all of our items at once, and we can also look at all of the bins at the same time along with the bins in the next couple of pods that are coming up. So we can do this optimization over the whole set of information in 100 milliseconds.”

Essentially, robots are far better at optimization within the planning side of Tetrising, while humans are (still) far better at the manipulation side, but that gap is closing as robots get more experienced at operating in clutter and contact. Amazon has had Vulcan stowing robots operating for over a year in live warehouses in Germany and Washington state to collect training data, and those robots have successfully stowed hundreds of thousands of items.

Stowing is of course only half of what Vulcan is designed to do. Picking offers all kinds of unique challenges too, and you can read our in-depth discussion with Parness on that topic right here.

As far as I can make out, Amazon’s warehouses are highly structured, extremely organized, very tidy, absolute raging messes. Everything in an Amazon warehouse is (usually) exactly where it’s supposed to be, which is typically jammed into some pseudorandom fabric bin the size of a shoebox along with a bunch of other pseudorandom crap. Somehow, this turns out to be the most space- and time-efficient way of doing things, because (as we’ve written about before) you have to consider the process of stowing items away in a warehouse as well as the process of picking them, and that involves some compromises in favor of space and speed.

For humans, this isn’t so much of a problem. When someone orders something on Amazon, a human can root around in those bins, shove some things out of the way, and then pull out the item that they’re looking for. This is exactly the sort of thing that robots tend to be terrible at, because not only is this process slightly different every single time, it’s also very hard to define exactly how humans go about it.

Related: Amazon’s Vulcan Robots Now Stow Items Faster Than Humans

As you might expect, Amazon has been working very very hard on this picking problem. Today at an event in Germany, the company announced Vulcan, a robotic system that can both stow and pick items at human(ish) speeds.

Last time we talked with Aaron Parness, the director of applied science at Amazon Robotics, our conversation was focused on stowing—putting items into bins. As part of today’s announcement, Amazon revealed that its robots are now slightly faster at stowing than the average human is. But in the stow context, there’s a limited amount that a robot really has to understand about what’s actually happening in the bin. Fundamentally, the stowing robot’s job is to squoosh whatever is currently in a bin as far to one side as possible in order to make enough room to cram a new item in. As long as the robot is at least somewhat careful not to crushify anything, it’s a relatively straightforward task, at least compared to picking.

The choices made when an item is stowed into a bin will affect how hard it is to get that item out of that bin later on—this is called “bin etiquette.” Amazon is trying to learn bin etiquette with AI to make picking more efficient.Amazon

The defining problem of picking, as far as robots are concerned, is sensing and manipulation in clutter. “It’s a naturally contact-rich task, and we have to plan on that contact and react to it,” Parness says. And it’s not enough to solve these problems slowly and carefully, because Amazon Robotics is trying to put robots in production, which means that its systems are being directly compared to a not-so-small army of humans who are doing this exact same job very efficiently.

“There’s a new science challenge here, which is to identify the right item,” explains Parness. The thing to understand about identifying items in an Amazon warehouse is that there are a lot of them: something like 400 million unique items. One single floor of an Amazon warehouse can easily contain 15,000 pods, which is over a million bins, and Amazon has several hundred warehouses. This is a lot of stuff.

In theory, Amazon knows exactly which items are in every single bin. Amazon also knows (again, in theory), the weight and dimensions of each of those items, and probably has some pictures of each item from previous times that the item has been stowed or picked. This is a great starting point for item identification, but as Parness points out, “We have lots of items that aren’t feature rich—imagine all of the different things you might get in a brown cardboard box.”

As challenging as it is to correctly identify an item in a bin that may be stuffed to the brim with nearly identical items, an even bigger challenge is actually getting that item that you just identified out of the bin. The hardware and software that humans have for doing this task is unmatched by any robot, which is always a problem, but the real complicating factor is dealing with items that are all jumbled together in a small fabric bin. And the picking process itself involves more than just extraction—once the item is out of the bin, you then have to get it to the next order-fulfillment step, which means dropping it into another bin or putting it on a conveyor or something.

“When we were originally starting out, we assumed we’d have to carry the item over some distance after we pulled it out of the bin,” explains Parness. “So we were thinking we needed pinch grasping.” A pinch grasp is when you grab something between a finger (or fingers) and your thumb, and at least for humans, it’s a versatile and reliable way of grabbing a wide variety of stuff. But as Parness notes, for robots in this context, it’s more complicated: “Even pinch grasping is not ideal because if you pinch the edge of a book, or the end of a plastic bag with something inside it, you don’t have pose control of the item and it may flop around unpredictably.”

At some point, Parness and his team realized that while an item did have to move farther than just out of the bin, it didn’t actually have to get moved by the picking robot itself. Instead, they came up with a lifting conveyor that positions itself directly outside of the bin being picked from, so that all the robot has to do is get the item out of the bin and onto the conveyor. “It doesn’t look that graceful right now,” admits Parness, but it’s a clever use of hardware to substantially simplify the manipulation problem, and has the side benefit of allowing the robot to work more efficiently, since the conveyor can move the item along while the arm starts working on the next pick.

Amazon’s robots have different techniques for extracting items from bins, using different gripping hardware depending on what needs to be picked. The type of end effector that the system chooses and the grasping approach depend on what the item is, where it is in the bin, and also what it’s next to. It’s a complicated planning problem that Amazon is tackling with AI, as Parness explains. “We’re starting to build foundation models of items, including properties like how squishy they are, how fragile they are, and whether they tend to get stuck on other items or no. So we’re trying to learn those things, and it’s early stage for us, but we think reasoning about item properties is going to be important to get to that level of reliability that we need.”

Reliability has to be superhigh for Amazon (and with many other commercial robotic deployments) simply because small errors multiplied over huge deployments result in an unacceptable amount of screwing up. There’s a very, very long tail of unusual things that Amazon’s robots might encounter when trying to extract an item from a bin. Even if there’s some particularly weird bin situation that might only show up once in a million picks, that still ends up happening many times per day on the scale at which Amazon operates. Fortunately for Amazon, they’ve got humans around, and part of the reason that this robotic system can be effective in production at all is that if the robot gets stuck, or even just sees a bin that it knows is likely to cause problems, it can just give up, route that particular item to a human picker, and move on to the next one.

The other new technique that Amazon is implementing is a sort of modern approach to “visual servoing,” where the robot watches itself move and then adjusts its movement based on what it sees. As Parness explains: “It’s an important capability because it allows us to catch problems before they happen. I think that’s probably our biggest innovation, and it spans not just our problem, but problems across robotics.”

Parness was very clear that (for better or worse) Amazon isn’t thinking about its stowing and picking robots in terms of replacing humans completely. There’s that long tail of items that need a human touch, and it’s frankly hard to imagine any robotic-manipulation system capable enough to make at least occasional human help unnecessary in an environment like an Amazon warehouse, which somehow manages to maximize organization and chaos at the same time.

These stowing and picking robots have been undergoing live testing in an Amazon warehouse in Germany for the past year, where they’re already demonstrating ways in which human workers could directly benefit from their presence. For example, Amazon pods can be up to 2.5 meters tall, meaning that human workers need to use a stepladder to reach the highest bins and bend down to reach the lowest ones. If the robots were primarily tasked with interacting with these bins, it would help humans work faster while putting less stress on their bodies.

With the robots so far managing to keep up with human workers, Parness tells us that the emphasis going forward will be primarily on getting better at not screwing up: “I think our speed is in a really good spot. The thing we’re focused on now is getting that last bit of reliability, and that will be our next year of work.” While it may seem like Amazon is optimizing for its own very specific use cases, Parness reiterates that the bigger picture here is using every last one of those 400 million items jumbled into bins as a unique opportunity to do fundamental research on fast, reliable manipulation in complex environments.

“If you can build the science to handle high contact and high clutter, we’re going to use it everywhere,” says Parness. “It’s going to be useful for everything, from warehouses to your own home. What we’re working on now are just the first problems that are forcing us to develop these capabilities, but I think it’s the future of robotic manipulation.”

As far as I can make out, Amazon’s warehouses are highly structured, extremely organized, very tidy, absolute raging messes. Everything in an Amazon warehouse is (usually) exactly where it’s supposed to be, which is typically jammed into some pseudorandom fabric bin the size of a shoebox along with a bunch of other pseudorandom crap. Somehow, this turns out to be the most space- and time-efficient way of doing things, because (as we’ve written about before) you have to consider the process of stowing items away in a warehouse as well as the process of picking them, and that involves some compromises in favor of space and speed.

For humans, this isn’t so much of a problem. When someone orders something on Amazon, a human can root around in those bins, shove some things out of the way, and then pull out the item that they’re looking for. This is exactly the sort of thing that robots tend to be terrible at, because not only is this process slightly different every single time, it’s also very hard to define exactly how humans go about it.

As you might expect, Amazon has been working very very hard on this picking problem. Today at an event in Germany, the company announced Vulcan, a robotic system that can both stow and pick items at human(ish) speeds.

Last time we talked with Aaron Parness, the director of applied science at Amazon Robotics, our conversation was focused on stowing—putting items into bins. As part of today’s announcement, Amazon revealed that its robots are now slightly faster at stowing than the average human is. But in the stow context, there’s a limited amount that a robot really has to understand about what’s actually happening in the bin. Fundamentally, the stowing robot’s job is to squoosh whatever is currently in a bin as far to one side as possible in order to make enough room to cram a new item in. As long as the robot is at least somewhat careful not to crushify anything, it’s a relatively straightforward task, at least compared to picking.

The choices made when an item is stowed into a bin will affect how hard it is to get that item out of that bin later on—this is called “bin etiquette.” Amazon is trying to learn bin etiquette with AI to make picking more efficient.Amazon

The defining problem of picking, as far as robots are concerned, is sensing and manipulation in clutter. “It’s a naturally contact-rich task, and we have to plan on that contact and react to it,” Parness says. And it’s not enough to solve these problems slowly and carefully, because Amazon Robotics is trying to put robots in production, which means that its systems are being directly compared to a not-so-small army of humans who are doing this exact same job very efficiently.

“There’s a new science challenge here, which is to identify the right item,” explains Parness. The thing to understand about identifying items in an Amazon warehouse is that there are a lot of them: something like 400 million unique items. One single floor of an Amazon warehouse can easily contain 15,000 pods, which is over a million bins, and Amazon has several hundred warehouses. This is a lot of stuff.

In theory, Amazon knows exactly which items are in every single bin. Amazon also knows (again, in theory), the weight and dimensions of each of those items, and probably has some pictures of each item from previous times that the item has been stowed or picked. This is a great starting point for item identification, but as Parness points out, “We have lots of items that aren’t feature rich—imagine all of the different things you might get in a brown cardboard box.”

As challenging as it is to correctly identify an item in a bin that may be stuffed to the brim with nearly identical items, an even bigger challenge is actually getting that item that you just identified out of the bin. The hardware and software that humans have for doing this task is unmatched by any robot, which is always a problem, but the real complicating factor is dealing with items that are all jumbled together in a small fabric bin. And the picking process itself involves more than just extraction—once the item is out of the bin, you then have to get it to the next order-fulfillment step, which means dropping it into another bin or putting it on a conveyor or something.

“When we were originally starting out, we assumed we’d have to carry the item over some distance after we pulled it out of the bin,” explains Parness. “So we were thinking we needed pinch grasping.” A pinch grasp is when you grab something between a finger (or fingers) and your thumb, and at least for humans, it’s a versatile and reliable way of grabbing a wide variety of stuff. But as Parness notes, for robots in this context, it’s more complicated: “Even pinch grasping is not ideal because if you pinch the edge of a book, or the end of a plastic bag with something inside it, you don’t have pose control of the item and it may flop around unpredictably.”

At some point, Parness and his team realized that while an item did have to move farther than just out of the bin, it didn’t actually have to get moved by the picking robot itself. Instead, they came up with a lifting conveyor that positions itself directly outside of the bin being picked from, so that all the robot has to do is get the item out of the bin and onto the conveyor. “It doesn’t look that graceful right now,” admits Parness, but it’s a clever use of hardware to substantially simplify the manipulation problem, and has the side benefit of allowing the robot to work more efficiently, since the conveyor can move the item along while the arm starts working on the next pick.

Amazon’s robots have different techniques for extracting items from bins, using different gripping hardware depending on what needs to be picked. The type of end effector that the system chooses and the grasping approach depend on what the item is, where it is in the bin, and also what it’s next to. It’s a complicated planning problem that Amazon is tackling with AI, as Parness explains. “We’re starting to build foundation models of items, including properties like how squishy they are, how fragile they are, and whether they tend to get stuck on other items or no. So we’re trying to learn those things, and it’s early stage for us, but we think reasoning about item properties is going to be important to get to that level of reliability that we need.”

Reliability has to be superhigh for Amazon (and with many other commercial robotic deployments) simply because small errors multiplied over huge deployments result in an unacceptable amount of screwing up. There’s a very, very long tail of unusual things that Amazon’s robots might encounter when trying to extract an item from a bin. Even if there’s some particularly weird bin situation that might only show up once in a million picks, that still ends up happening many times per day on the scale at which Amazon operates. Fortunately for Amazon, they’ve got humans around, and part of the reason that this robotic system can be effective in production at all is that if the robot gets stuck, or even just sees a bin that it knows is likely to cause problems, it can just give up, route that particular item to a human picker, and move on to the next one.

The other new technique that Amazon is implementing is a sort of modern approach to “visual servoing,” where the robot watches itself move and then adjusts its movement based on what it sees. As Parness explains: “It’s an important capability because it allows us to catch problems before they happen. I think that’s probably our biggest innovation, and it spans not just our problem, but problems across robotics.”

Parness was very clear that (for better or worse) Amazon isn’t thinking about its stowing and picking robots in terms of replacing humans completely. There’s that long tail of items that need a human touch, and it’s frankly hard to imagine any robotic-manipulation system capable enough to make at least occasional human help unnecessary in an environment like an Amazon warehouse, which somehow manages to maximize organization and chaos at the same time.

These stowing and picking robots have been undergoing live testing in an Amazon warehouse in Germany for the past year, where they’re already demonstrating ways in which human workers could directly benefit from their presence. For example, Amazon pods can be up to 2.5 meters tall, meaning that human workers need to use a stepladder to reach the highest bins and bend down to reach the lowest ones. If the robots were primarily tasked with interacting with these bins, it would help humans work faster while putting less stress on their bodies.

With the robots so far managing to keep up with human workers, Parness tells us that the emphasis going forward will be primarily on getting better at not screwing up: “I think our speed is in a really good spot. The thing we’re focused on now is getting that last bit of reliability, and that will be our next year of work.” While it may seem like Amazon is optimizing for its own very specific use cases, Parness reiterates that the bigger picture here is using every last one of those 400 million items jumbled into bins as a unique opportunity to do fundamental research on fast, reliable manipulation in complex environments.

“If you can build the science to handle high contact and high clutter, we’re going to use it everywhere,” says Parness. “It’s going to be useful for everything, from warehouses to your own home. What we’re working on now are just the first problems that are forcing us to develop these capabilities, but I think it’s the future of robotic manipulation.”

Video Friday is your weekly selection of awesome robotics videos, collected by your friends at IEEE Spectrum robotics. We also post a weekly calendar of upcoming robotics events for the next few months. Please send us your events for inclusion.

Enjoy today’s videos!

[ DEEP Robotics ]

The latest OK Go music video includes lots of robots.

And here’s a bit more on how it was done, mostly with arms from Universal Robots.

[ OK Go ]

[ Berkeley Humanoid Lite ]

I think this may be the first time I’ve ever seen a pedestal-mounted Atlas from Boston Dynamics.

[ NVIDIA ]

[ University of Bath ]

Whenever I see a soft robot, I have to ask, “Okay, but how soft is it really?” And usually, there’s a pump or something hidden away off-camera somewhere. So it’s always cool to see actually soft robotics actuators, like these, which are based on phase-changing water.

[ Nature Communications ] via [ Collaborative Robotics Laboratory, University of Coimbra ]

Thanks, Pedro!

[ Paper ] via [ IEEE Robotics and Automation Magazine ]

Thanks, Bram!

[ Waymo ]

[ Robotics & Mechanisms Laboratory ]

[ PNDbotics ]

[ Burger Bots ]

Look at this little guy, such a jaunty walk!

[ Science Advances ]

[ Hybrid Robotics ]

It’s debatable whether this is technically a robot, but sure, let’s go with it, because it’s pretty neat—a cable car of sorts consisting of a soft twisted ring that’s powered by infrared light.

[ North Carolina State University ]

[ Bloomberg ]

[ University of Pennsylvania ]

I come from dairy-farming stock. My grandfather, the original Harry Goldstein, owned a herd of dairy cows and a creamery in Louisville, Ky., that bore the family name. One fateful day in early April 1944, Harry was milking his cows when a heavy metallic part of his homemade milking contraption—likely some version of the then-popular Surge Bucket Milker—struck him in the abdomen, causing a blood clot that ultimately led to cardiac arrest and his subsequent demise a few days later, at the age of 48.

Fast forward 80 years and dairy farming is still a dangerous occupation. According to an analysis of U.S. Bureau of Labor Statistics data done by the advocacy group Farmworker Justice, the U.S. dairy industry recorded 223 injuries per 10,000 full-time workers in 2020, almost double the rate for all of private industry combined. Contact with animals tops the list of occupational hazards for dairy workers, followed by slips, trips, and falls. Other significant risks include contact with objects or equipment, overexertion, and exposure to toxic substances. Every year, a few dozen dairy workers in the United States meet a fate similar to my grandfather’s, with 31 reported deadly accidents on dairy farms in 2021.

As Senior Editor Evan Ackerman notes in “Robots for Cows (and Their Humans)”, traditional dairy farming is very labor-intensive. Cows need to be milked at least twice per day to prevent discomfort. Conventional milking facilities are engineered for human efficiency, with systems like rotating carousels that bring the cows to the dairy workers.

The robotic systems that Netherlands-based Lely has been developing since the early 1990s are much more about doing things the bovine way. That includes letting the cows choose when to visit the milking robot, resulting in a happier herd and up to 10 percent more milk production.

Turns out that what’s good for the cows might be good for the humans, too. Another Lely bot deals with feeding, while yet another mops up the manure, the proximate cause of much of the slipping and sliding that can result in injuries. The robots tend to reset the cow–human relationship—it becomes less adversarial because the humans aren’t always there bossing the cows around.

Farmer well-being is also enhanced because the humans don’t have to be around to tempt fate, and they can spend time doing other things, freed up by the robot laborers. In fact, when Ackerman visited Lely’s demonstration farm in Schipluiden, Netherlands, to see the Lely robots in action, he says, “The original plan was for me to interview the farmer, and he was just not there at all for the entire visit while the cows were getting milked by the robots. In retrospect, that might have been the most effective way he could communicate how these robots are changing work for dairy farmers.”

The farmer’s absence also speaks volumes about how far dairy technology has evolved since my grandfather’s day. Harry Goldstein’s life was cut short by the very equipment he hacked to make his own work easier. Today’s dairy-farming innovations aren’t just improving efficiency—they’re keeping humans out of harm’s way entirely. In the dairy farms of the future, the most valuable safety features might simply be a barn resounding with the whirring of robots and moos of contentment.

I come from dairy-farming stock. My grandfather, the original Harry Goldstein, owned a herd of dairy cows and a creamery in Louisville, Ky., that bore the family name. One fateful day in early April 1944, Harry was milking his cows when a heavy metallic part of his homemade milking contraption—likely some version of the then-popular Surge Bucket Milker—struck him in the abdomen, causing a blood clot that ultimately led to cardiac arrest and his subsequent demise a few days later, at the age of 48.

Fast forward 80 years and dairy farming is still a dangerous occupation. According to an analysis of U.S. Bureau of Labor Statistics data done by the advocacy group Farmworker Justice, the U.S. dairy industry recorded 223 injuries per 10,000 full-time workers in 2020, almost double the rate for all of private industry combined. Contact with animals tops the list of occupational hazards for dairy workers, followed by slips, trips, and falls. Other significant risks include contact with objects or equipment, overexertion, and exposure to toxic substances. Every year, a few dozen dairy workers in the United States meet a fate similar to my grandfather’s, with 31 reported deadly accidents on dairy farms in 2021.

As Senior Editor Evan Ackerman notes in “Robots for Cows (and Their Humans)”, traditional dairy farming is very labor-intensive. Cows need to be milked at least twice per day to prevent discomfort. Conventional milking facilities are engineered for human efficiency, with systems like rotating carousels that bring the cows to the dairy workers.

The robotic systems that Netherlands-based Lely has been developing since the early 1990s are much more about doing things the bovine way. That includes letting the cows choose when to visit the milking robot, resulting in a happier herd and up to 10 percent more milk production.

Turns out that what’s good for the cows might be good for the humans, too. Another Lely bot deals with feeding, while yet another mops up the manure, the proximate cause of much of the slipping and sliding that can result in injuries. The robots tend to reset the cow–human relationship—it becomes less adversarial because the humans aren’t always there bossing the cows around.

Farmer well-being is also enhanced because the humans don’t have to be around to tempt fate, and they can spend time doing other things, freed up by the robot laborers. In fact, when Ackerman visited Lely’s demonstration farm in Schipluiden, Netherlands, to see the Lely robots in action, he says, “The original plan was for me to interview the farmer, and he was just not there at all for the entire visit while the cows were getting milked by the robots. In retrospect, that might have been the most effective way he could communicate how these robots are changing work for dairy farmers.”

The farmer’s absence also speaks volumes about how far dairy technology has evolved since my grandfather’s day. Harry Goldstein’s life was cut short by the very equipment he hacked to make his own work easier. Today’s dairy-farming innovations aren’t just improving efficiency—they’re keeping humans out of harm’s way entirely. In the dairy farms of the future, the most valuable safety features might simply be a barn resounding with the whirring of robots and moos of contentment.

Meet FREDERICK Mark 2, the Friendly Robot for Education, Discussion and Entertainment, the Retrieval of Information, and the Collation of Knowledge, better known as Freddy II. This remarkable robot could put together a simple model car from an assortment of parts dumped in its workspace. Its video-camera eyes and pincer hand identified and sorted the individual pieces before assembling the desired end product. But onlookers had to be patient. Assembly took about 16 hours, and that was after a day or two of “learning” and programming.

Freddy II was completed in 1973 as one of a series of research robots developed by Donald Michie and his team at the University of Edinburgh during the 1960s and ’70s. The robots became the focus of an intense debate over the future of AI in the United Kingdom. Michie eventually lost, his funding was gutted, and the ensuing AI winter set back U.K. research in the field for a decade.

In 1967, Donald Michie, along with Richard Gregory and Hugh Christopher Longuet-Higgins, founded the Department of Machine Intelligence and Perception at the University of Edinburgh with the near-term goal of developing a semiautomated robot and then longer-term vision of programming “integrated cognitive systems,” or what other people might call intelligent robots. At the time, the U.S. Defense Advanced Research Projects Agency and Japan’s Computer Usage Development Institute were both considering plans to create fully automated factories within a decade. The team at Edinburgh thought they should get in on the action too.

Two years later, Stephen Salter and Harry G. Barrow joined Michie and got to work on Freddy I. Salter devised the hardware while Barrow designed and wrote the software and computer interfacing. The resulting simple robot worked, but it was crude. The AI researcher Jean Hayes (who would marry Michie in 1971) referred to this iteration of Freddy as an “arthritic Lady of Shalott.”

Freddy I consisted of a robotic arm, a camera, a set of wheels, and some bumpers to detect obstacles. Instead of roaming freely, it remained stationary while a small platform moved beneath it. Barrow developed an adaptable program that enabled Freddy I to recognize irregular objects. In 1969, Salter and Barrow published in Machine Intelligence their results, “Design of Low-Cost Equipment for Cognitive Robot Research,” which included suggestions for the next iteration of the robot.

Freddy I, completed in 1969, could recognize objects placed in front of it—in this case, a teacup.University of Edinburgh

More people joined the team to build Freddy Mark 1.5, which they finished in May 1971. Freddy 1.5 was a true robotic hand-eye system. The hand consisted of two vertical, parallel plates that could grip an object and lift it off the platform. The eyes were two cameras: one looking directly down on the platform, and the other mounted obliquely on the truss that suspended the hand over the platform. Freddy 1.5’s world was a 2-meter by 2-meter square platform that moved in an x-y plane.

Freddy 1.5 quickly morphed into Freddy II as the team continued to grow. Improvements included force transducers added to the “wrist” that could deduce the strength of the grip, the weight of the object held, and whether it had collided with an object. But what really set Freddy II apart was its versatile assembly program: The robot could be taught to recognize the shapes of various parts, and then after a day or two of programming, it could assemble simple models. The various steps can be seen in this extended video, narrated by Barrow:

And then what happened? So much. But before I get into all that, let me just say that rarely do I, as a historian, have the luxury of having my subjects clearly articulate the aims of their projects, imagine the future, and then, years later, reflect on their experiences. As a cherry on top of this historian’s delight, the topic at hand—artificial intelligence—also happens to be of current interest to pretty much everyone.

As with many fascinating histories of technology, events turn on a healthy dose of professional bickering. In this case, the disputants were Michie and the applied mathematician James Lighthill, who had drastically different ideas about the direction of robotics research. Lighthill favored applied research, while Michie was more interested in the theoretical and experimental possibilities. Their fight escalated quickly, became public with a televised debate on the BBC, and concluded with the demise of an entire research field in Britain.

A damning report in 1973 by applied mathematician James Lighthill [left] resulted in funding being pulled from the AI and robotics program led by Donald Michie [right]. Left: Chronicle/Alamy; Right: University of Edinburgh

It all started in September 1971, when the British Science Research Council, which distributed public funds for scientific research, commissioned Lighthill to survey the state of academic research in artificial intelligence. The SRC was finding it difficult to make informed funding decisions in AI, given the field’s complexity. It suspected that some AI researchers’ interests were too narrowly focused, while others might be outright charlatans. Lighthill was called in to give the SRC a road map.

No intellectual slouch, Lighthill was the Lucasian Professor of Mathematics at the University of Cambridge, a position also held by Isaac Newton, Charles Babbage, and Stephen Hawking. Lighthill solicited input from scholars in the field and completed his report in March 1972. Officially titled “ Artificial Intelligence: A General Survey,” but informally called the Lighthill Report, it divided AI into three broad categories: A, for advanced automation; B, for building robots, but also bridge activities between categories A and C; and C, for computer-based central nervous system research. Lighthill acknowledged some progress in categories A and C, as well as a few disappointments.

Lighthill viewed Category B, though, as a complete failure. “Progress in category B has been even slower and more discouraging,” he wrote, “tending to sap confidence in whether the field of research called AI has any true coherence.” For good measure, he added, “AI not only fails to take the first fence but ignores the rest of the steeplechase altogether.” So very British.

Lighthill concluded his report with his view of the next 25 years in AI. He predicted a “fission of the field of AI research,” with some tempered optimism for achievement in categories A and C but a valley of continued failures in category B. Success would come in fields with clear applications, he argued, but basic research was a lost cause.

The Science Research Council published Lighthill’s report the following year, with responses from N. Stuart Sutherland of the University of Sussex and Roger M. Needham of the University of Cambridge, as well as Michie and his colleague Longuet-Higgins.

Sutherland sought to relabel category B as “basic research in AI” and to have the SRC increase funding for it. Needham mostly supported Lighthill’s conclusions and called for the elimination of the term AI—“a rather pernicious label to attach to a very mixed bunch of activities, and one could argue that the sooner we forget it the better.”

Longuet-Higgins focused on his own area of interest, cognitive science, and ended with an ominous warning that any spin-off of advanced automation would be “more likely to inflict multiple injuries on human society,” but he didn’t explain what those might be.

Michie, as the United Kingdom’s academic leader in robots and machine intelligence, understandably saw the Lighthill Report as a direct attack on his research agenda. With his funding at stake, he provided the most critical response, questioning the very foundation of the survey: Did Lighthill talk with any international experts? How did he overcome his own biases? Did he have any sources and references that others could check? He ended with a request for more funding—specifically the purchase of a DEC System 10 (also known as the PDP-10) mainframe computer. According to Michie, if his plan were followed, Britain would be internationally competitive in AI by the end of the decade.

After Michie’s funding was cut, the many researchers affiliated with his bustling lab lost their jobs.University of Edinburgh

This whole affair might have remained an academic dispute, but then the BBC decided to include a debate between Lighthill and a panel of experts as part of its “Controversy” TV series. “Controversy” was an experiment to engage the public in science. On 9 May 1973, an interested but nonspecialist audience filled the auditorium at the Royal Institution in London to hear the debate.

Lighthill started with a review of his report, explaining the differences he saw between automation and what he called “the mirage” of general-purpose robots. Michie responded with a short film of Freddy II assembling a model, explaining how the robot processes information. Michie argued that AI is a subject with its own purposes, its own criteria, and its own professional standards.

After a brief back and forth between Lighthill and Michie, the show’s host turned to the other panelists: John McCarthy, a professor of computer science at Stanford University, and Richard Gregory, a professor in the department of anatomy at the University of Bristol who had been Michie’s colleague at Edinburgh. McCarthy, who coined the term artificial intelligence in 1955, supported Michie’s position that AI should be its own area of research, not simply a bridge between automation and a robot that mimics a human brain. Gregory described how the work of Michie and McCarthy had influenced the field of psychology.

You can watch the debate or read a transcript.

Despite international support from the AI community, though, the SRC sided with Lighthill and gutted funding for AI and robotics; Michie had lost. Michie’s bustling lab went from being an international center of research to just Michie, a technician, and an administrative assistant. The loss ushered in the first British AI winter, with the United Kingdom making little progress in the field for a decade.

For his part, Michie pivoted and recovered. He decommissioned Freddy II in 1980, at which point it moved to the Royal Museum of Scotland (now the National Museum of Scotland), and he replaced it with a Unimation PUMA robot.

In 1983, Michie founded the Turing Institute in Glasgow, an AI lab that worked with industry on both basic and applied research. The year before, he had written Machine Intelligence and Related Topics: An Information Scientist’s Weekend Book (Gordon and Breach). Michie intended it as intellectual musings that he hoped scientists would read, perhaps on the weekend, to help them get beyond the pursuits of the workweek. The book is wide-ranging, covering his three decades of work.

In the introduction to the chapters covering Freddy and the aftermath of the Lighthill report, Michie wrote, perhaps with an eye toward history:

History has indeed rendered judgment on the debate and the Lighthill Report. In 2019, for example, computer scientist Maarten van Emden, a colleague of Michie’s, reflected on the demise of the Freddy project with these choice words for Lighthill: “a pompous idiot who lent himself to produce a flaky report to serve as a blatantly inadequate cover for a hatchet job.”

And in a March 2024 post on GitHub, the blockchain entrepreneur Jeffrey Emanuel thoughtfully dissected Lighthill’s comments and the debate itself. Of Lighthill, he wrote, “I think we can all learn a very valuable lesson from this episode about the dangers of overconfidence and the importance of keeping an open mind. The fact that such a brilliant and learned person could be so confidently wrong about something so important should give us pause.”

Arguably, both Lighthill and Michie correctly predicted certain aspects of the AI future while failing to anticipate others. On the surface, the report and the debate could be described as simply about funding. But it was also more fundamentally about the role of academic research in shaping science and engineering and, by extension, society. Ideally, universities can support both applied research and more theoretical work. When funds are limited, though, choices are made. Lighthill chose applied automation as the future, leaving research in AI and machine intelligence in the cold.

It helps to take the long view. Over the decades, AI research has cycled through several periods of spring and winter, boom and bust. We’re currently in another AI boom. Is this time different? No one can be certain what lies just over the horizon, of course. That very uncertainty is, I think, the best argument for supporting people to experiment and conduct research into fundamental questions, so that they may help all of us to dream up the next big thing.

Part of a continuing series looking at historical artifacts that embrace the boundless potential of technology.

An abridged version of this article appears in the May 2025 print issue as “This Robot Was the Fall Guy for British AI.”

Donald Michie’s lab regularly published articles on the group’s progress, especially in Machine Intelligence, a journal founded by Michie.

The Lighthill Report and recordings of the debate are both available in their entirety online—primary sources that capture the intensity of the moment.

In 2009, a group of alumni from Michie’s Edinburgh lab, including Harry Barrow and Pat Fothergill (formerly Ambler), created a website to share their memories of working on Freddy. The site offers great firsthand accounts of the development of the robot. Unfortunately for the historian, they didn’t explore the lasting effects of the experience. A decade later, though, Maarten van Emden did, in his 2019 article “Reflecting Back on the Lighthill Affair,” in the IEEE Annals of the History of Computing.

Beyond his academic articles, Michie was a prolific author. Two collections of essays I found particularly useful are On Machine Intelligence (John Wiley & Sons, 1974) and Machine Intelligence and Related Topics: An Information Scientist’s Weekend Book (Gordon and Breach, 1982).

Jon Agar’s 2020 article “What Is Science for? The Lighthill Report on Artificial Intelligence Reinterpreted” and Jeffrey Emanuel’s GitHub post offer historical interpretations on this mostly forgotten blip in the history of robotics and artificial intelligence.