The latest robotics and AI news, aggregated from top sources every 6 hours.
Ultrasound sensing can provide tactile perception for robotic hands while avoiding wear and tear, says UltraSense. The post Ultrasound offers a scalable path to tactile intelligence for physical AI appeared first on The Robot Report.
CEO Salar al Khafaji introduces podcast listeners to how Monumental is using robotics to build walls. The post One brick at a time: How Monumental uses robotics to build walls appeared first on The Robot Report.
ASI CEO Mel Torrie will explore why the intersection of autonomous vehicles and robotics will further revolutionize field deployments. The post Learn how AVs and robotics are laying the groundwork for field deployments at RoboBusiness appeared first on The Robot Report.
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.Humanoids Summit Seoul: 22–23 September 2026, SEOULIROS 2026: 27 September–1 October 2026, PITTSBURGHCoRL 2026: 9–12 November 2026, AUSTINEnjoy today’s videos! Traversing sparse 3D structures requires humanoid robots to perceive thin, overhanging geometry while executing agile, accurate whole-body motions. We study this problem through monkey-bar traversal, where the robot must jump to the structure, traverse it through sparse bar interactions, and land safely.The list of obstacles that you can traverse to escape a robot is getting shorter.[ ETH Zurich Robotic Systems Lab ]YES GIVE ROBOTS TWO HEADS I LOVE IT![ General Robotics Lab ]9/11 was the first documented use of robots for urban search and rescue and helped create the field of disaster robotics. Personnel began assembling on the afternoon of September 11 and worked the pile from late on September 11 through October 2, when the last available robot failed. The robots found no survivors, but they located remains and helped search for routes through the rubble toward basements and stairwells where trapped firefighters might have gone.[ CRASAR ]Unitree majorly fully open-sources the UnifoLM-WLA-1.0 embodied foundation model, achieving new SOTA results across multiple benchmarks among open-source models worldwide. A single model coordinates desktop and whole-body mobile manipulation, supporting cross-task and cross-end-effector generalization, driven by one model, whole-body coordination.[ Unitree ]Compliance is very important in physical interaction. In this work, we show how a multilined aerial robot uses its centroid and joint motion to achieve hybrid impedance–admittance control in contact-rich aerial manipulation tasks such as surface sliding. This work will be presented in IEEE IROS 2026.[ DRAGON Lab ]Thanks, Moju!Remind me not to get too close to this.[ RaiLab Kaist ]Welcome to this edition of Things That Really Seem Like They Should Not Fly.[ Texas A&M University Advanced Vertical Flight Lab ]Achieving agile and generalized legged locomotion across terrains requires tight integration of perception and control, especially under occlusions and sparse footholds. Existing methods have demonstrated agility on parkour courses but often rely on end-to-end sensorimotor models with limited generalization and interpretability. By contrast, methods targeting generalized locomotion typically exhibit limited agility and struggle with visual occlusions. We introduce a unified reinforcement learning (RL) framework for agile and generalized locomotion that incorporates a novel attention-based map encoder in the control policy.[ ETH Zurich Robotic Systems Lab ]Finally, the killer app for humanoid robots! But we probably shouldn’t call it that.[ Unitree ]I suspect that this demo avoids many of the things that are actually difficult about doing dishes. Not just the water and the slippery soapiness, but also identifying when a dish is dirty as well as when it is actually clean.[ Flexiv ]Sure, I guess I might want a robot to deliver a burrito to me while I’m hiking to the top of a mountain in the rain...?[ DEEP Robotics ]AI has transformed the digital world. It writes our code, generates our images, reasons in our language. But the physical world—the plants that make our power, our fuel, our steel and chemicals—it has barely touched. ANYbotics CEO and Co-Founder Péter Fankhauser on the bet behind the company: why legged robots turned out to be the way into the world’s most demanding industrial plants, what it took to certify one for explosive atmospheres after experts called it impossible, and where autonomous industrial work goes next.[ ANYbotics ]
From 25–27 August, UK RAS STEPS members came together at the University of York’s Institute for Safe Autonomy for a three-day Micromaze Robot Hackathon. Working in teams, participants were challenged to design, build and program autonomous robots capable of navigating a series of increasingly complex mazes. Each team used a custom Raspberry Pi Pico W […]
Researchers have found a way to perform certain quantum operations more than 1,000 times faster, cutting thousands of repeated control cycles down to just one. The advance could reduce errors and bring reliable, fault-tolerant quantum computers closer to reality.
Simplicity—combined with the difficulty of getting stuff done—makes ClickFix ideal.
Dexterous manipulation remains one of the biggest barriers keeping robots from successfully tackling a wide range of everyday tasks. A sense of touch could be the key, but a lack of quality data has held back progress. This is now starting to change as academic labs and startups race to build new tactile datasets and techniques to put them to use.Over the last few years, vision-language-action (VLA) models have significantly improved the ability of robots to carry out complex tasks involving objects and environments they’ve never encountered before. Pretrained on huge amounts of images, video, and text, and then fine-tuned on a smaller number of teleoperated robot demonstrations, these VLAs can guide robots through a growing range of everyday jobs—like folding laundry, tidying living rooms, and even operating kitchen gadgets—using just a video feed and natural language instructions.But robots still struggle with tasks that require fine-grained hand control, such as handling deformable materials or manipulating small objects—plugging in a USB cable or turning a key in a lock, for example. That’s partly because VLAs ignore one of the primary sources of information humans rely on in these situations: tactile feedback.Manipulating Like Humans“Most dexterous manipulation can be done by humans with their eyes closed,” says Trevor Darrell, professor of computer science at the University of California, Berkeley. “Understanding force, slip, and precise grasping is not something that can be done well with traditional vision sensors.”However, making effective use of tactile sensors is difficult. Tactile sensor data has very different characteristics to the image data VLAs are normally trained on, and tactile datasets lag far behind the internet-scale of many vision and language datasets. To get around this, Darrell’s team devised a way to first pretrain a model on existing datasets before giving it a sense of touch by training a specialist submodel on 100 hours of specially collected, high-quality tactile data including demonstrations of common actions like wiping, grasping, twisting, or pouring using more than 200 different household objects. Explore an interactive visualizer of a small portion of the T-Rex dataset. T-RexPutting the tactile data to use was not straightforward. The goal was for a robot to be able to use the tactile signal to correct its grip in real time as it manipulated objects. But this requires reaction times faster than most vision-language models operate at. This mismatch is a significant challenge, says Darrell, so the team used separate submodels, known as “experts,” to handle high-level actions and low-level tactile control in a way that’s quick enough for the tactile feedback to be useful.The action expert produces motion plans, while the tactile expert, which operates four times faster, uses tactile feedback to adjust the motion plan in real time based on what the robot is feeling as it goes. The model was then fine-tuned on about 100 teleoperated demonstrations of relatively complex manipulation tasks, such as screwing in a light bulb, applying toothpaste to a toothbrush, or transferring an egg between trays, where it averaged a success rate of 65 percent across 12 tasks—nearly double the best VLA model.Data DiversityOne limitation, admits Darrell, is that his data comes from a single instance of robotic hardware. Robot hands range from fully articulated five-finger designs to simple pincer grippers, and tactile sensors can rely on fundamentally different physics, from measuring changes in resistance to recording images of a soft gel pad deforming. That makes most tactile AI research sensor-specific, says Chengbo Yuan, a master’s student at Tsinghua University in Beijing, and makes it hard to share data and transfer learnings between groups.Yuan recently set out to tackle this problem by aggregating more than 3,000 hours of tactile robotic data from publicly available datasets, covering 21 sensor types and a variety of robot embodiments. Yuan says they were inspired by efforts like the Open X-Embodiment collaboration, which pooled data from many robots and led to models that generalize to hardware not used in training. Yuan’s team then designed a hardware-agnostic model that can train on this diverse data by converting each sensor’s output into a shared format and mapping it onto labeled positions on a template of a human hand. This model was much more successful than a baseline model, even on hardware it had never encountered before. Yuan puts that down to it acquiring “some kind of common sense of tactile knowledge,” by training on such diverse setups.Chasing ScaleDespite the promising results, Yuan thinks more tactile data is needed, and his group is now leading an 80-institution collaboration to collate a larger set of teleoperated demonstrations using a standardized approach to tactile data collection and processing. In the meantime, Fudan University in Shanghai and its spin-out NeoteAI have already produced a tactile dataset an order of magnitude larger than previous efforts. Using a proprietary sensor attached to a variety of robotic arms and a handheld gripper operated by humans, they have collected more than 30,000 hours of demonstrations with synchronized visual and tactile data.The researchers used this data to train a model that doesn’t just react to touch, but also proactively predicts what the robot should be feeling to help guide and assess actions, significantly improving performance. Shunlin Lu, a postdoc researcher at Fudan University and CTO of NeoteAI, says the results are clear evidence that access to large-scale and diverse tactile data leads to significant performance gains. Robot manipulation policies with a tactile component offer improved performance on a variety of real-world tasks.NeoteAIAnother approach to scaling tactile data could be to piggyback on the vast quantities of visual robotics data already collected. Researchers at the University of Southern California, in Los Angeles, recently released a model that learned to infer tactile information from visual data, by training it on more than 2,700 demonstrations of everyday manipulation using a handheld gripper that records both tactile data and images from a camera on the device. The model learned associations between images of the gripper coming into contact with objects and the amount of pressure felt by the tactile sensors at that moment, giving even robots without tactile sensors a rudimentary sense of touch that the researchers showed to be particularly useful for contact-rich manipulation tasks. But their broader ambition is to use the generator to add tactile data to existing vision datasets.How much tactile data will be required for breakthroughs in dexterous tasks remains unclear. So far, tactile training’s main contribution has been to make robots more efficient learners at tasks already within reach like picking and placing objects, says Yuan, and he suspects new algorithms may be required to tackle problems truly impossible without touch.Long Cheng of the Chinese Academy of Sciences also thinks raw data is no panacea. “Data is good,” he says. “But how to use them correctly is another issue.” The problem, he notes, is that vision provides a continuous, high-bandwidth stream of pixels, while tactile signals are sparse and intermittent, so models learn to ignore them. His solution, being presented at IROS 2026 later this month, is a model that predicts what a robot will feel from vision alone and then compares it against real tactile input. A large gap between the two means the sensor is detecting something the robot would otherwise miss, so these surprising signals are amplified while predictable ones are dampened. Across five contact-rich tasks the approach averaged 62.8% success against 28.2% for the same model without touch.Lu is more confident that data scaling could have similar benefits to those seen in areas like language and vision. He guesses closer to 100,000 hours, collected in varied, real-world settings rather than in the lab, could unlock new capabilities. Either way, the field now has some early signs that larger tactile datasets and smarter ways to use them can give robots a significant boost on some of the most challenging tasks. “I think tactile intelligence is actually the next step for physical AI,” says Lu.
A patch gap and the hastened pace of AI-based vulnerability discovery are likely contributors.
US robotics researchers and industry are looking for a cohesive national robotics strategy that will maintain basic research and innovation while improving domestic production and adoption of robots. By Ellen H. Rumley and Allison Okamura A leader in tech innovation Robots are a crowd-pleasing example of American innovation and global technological leadership. The latest press […]
Security gnomes are pumping out patches ahead of an expected onslaught of AI-assisted attacks.
You sit down and put your arm in the cradle. You press a button. The machine takes it from there.A near-infrared light sweeps your inner elbow, hunting for a vein. A puff of alcohol hits your skin. An ultrasound probe glides across the arm, mapping how deep the vessel runs and which way it bends. Doppler captures the direction of blood flow to rule out the artery.The cuff tightens around your upper arm. The needle comes down and pierces the skin. Your blood flows into the collection tubes, each one tipped end-over-end nine times—no more, no less. The needle withdraws. You get a bandage. No human ever touched you.This is what it’s like to have blood taken by Aletta, the first autonomous blood-draw device authorized for use in the United States. Developed by the Dutch medical robotics firm Vitestro, the system combines imaging technologies with advanced robotics and AI to do by algorithm what a human phlebotomist—a trained healthcare professional who finds veins and draws blood by hand—does by feel.The U.S. Food and Drug Administration gave Aletta the go-ahead on 19 August for use on adults in non-hospitalized settings. The decision follows the lead of European regulators, who authorized the device two years earlier.“You have to tip your cap to them,” says Max Balter, a surgical robotics specialist at Medtronic who worked on autonomous blood-draw systems during grad school in the mid-2010s. “The engineering that they have is incredible… and with their FDA clearance, it moves the whole industry forward.”Aletta Boosts Lab Capacity Amid ShortagesIn a clinical trial involving more than 1,600 people in the Netherlands, Aletta successfully drew blood on the first attempt in 94.5 percent of cases, even among those with hard-to-access veins, people with obesity, and the elderly. When Aletta failed to identify a suitable vein, the patient was referred for conventional phlebotomy.“It’s exceptional performance,” says Joe El-Khoury, a clinical chemist at Yale who was not involved in the Dutch trial. “It’s definitely as good if not better” than a typical professional phlebotomist.Complications were minimal, with multiple built-in safeguards to detect problems, such as sensors that track arm movement and needle position, and halt the process should something go awry. And for those whose veins proved too challenging, a phlebotomist remains on hand to take over when needed.Notably, because a single phlebotomist can supervise up to three Aletta machines, the system should go a long way toward “helping clinical labs address the critical operational challenges related to the staffing shortages of phlebotomists,” says Luuk Giesen, chief medical officer of Vitestro.That’s no small challenge in a profession with a median annual turnover rate of nearly 25 percent and a vacancy rate of close to 10 percent, according to surveys of medical laboratories that draw mostly from U.S. institutions. The resulting staffing shortages can limit labs’ capacity to meet demand for routine diagnostic testing of blood counts, cholesterol, metabolic markers, and more. Aletta could address that bottleneck.Addressing Skin Tone Bias in Blood Draw AIThe promise of greater capacity, however, comes with a caveat: Aletta still fails in roughly one case out of 20. Who are those people?Some may simply have elusive or unusually deep veins. But a more significant obstacle could be skin pigmentation. In particular, the melanin in darker skin can interfere with the near-infrared light Aletta uses to first map the veins near the skin’s surface and identify promising puncture sites. The technique relies on hemoglobin absorbing the light differently from surrounding tissue, and darker skin tones can absorb more of that light before it reaches the camera, thereby reducing the contrast. Some experts have raised concerns that Aletta’s infrared sensors will perform poorly for people with darker skin tones, but Vitestro says that its machine also includes an ultrasound sensor in part to mitigate that risk.VitestroGiesen recognizes the issue could affect first-pass imaging, but notes that the main determinant of vein selection and needle placement is the ultrasound system, which relies on sound rather than light and should not be affected by skin pigmentation in the same way. “Ultrasound is skin-tone agnostic,” he says, adding that Vitestro has unpublished data showing no effect of skin tone on the system’s performance.The company thus claims on its website that the “technology works well for all skin tones,” an assertion echoed in the FDA press release announcing the authorization of Vitestro’s device.But given the history of racial disparities in medical devices—particularly optical technologies such as pulse oximeters, which can be less accurate in people with darker skin and went largely unrecognized as a problem for decades—such claims warrant evidence, says El-Khoury, who has written about the issue.Brooke Katzman, a clinical chemist at the Mayo Clinic who is collaborating with Vitestro, also wants more evidence that samples collected by the robot are as suitable for testing as those drawn by hand.The Dutch trial reported little damage to red blood cells, but other measures of sample quality, including clotted tubes, insufficient blood and proper tube filling, still need to be assessed, as do the results of routine laboratory tests themselves. Katzman plans to launch a U.S.-based trial next year to collect just that sort of data.“We’re going to do our due diligence,” she says. “Like any instrument we would bring into the lab, we’re going to put it through its paces before using it clinically.”The Future of Automated Blood TestingAletta takes its name from the 19th-century physician Aletta Jacobs, the first female doctor in the Netherlands and founder of what is widely considered the world’s first birth control clinic.That nod to history is fitting for a technology that builds on decades of research in robotic phlebotomy by groups in Europe, the United States, and China, and MagicNurse. Yet few pushed the concept as far as biomedical engineer Martin Yarmush of Rutgers University in New Jersey, in whose lab Medtronic’s Balter completed his Ph.D.In one version of their platform, the Rutgers team even coupled their robot to a benchtop blood analyzer, allowing it to draw samples and then measure levels of infection-fighting immune cells and oxygen-carrying red blood cells—all within minutes.That all-in system never made it out of laboratory testing. And VascuLogic, the company spun out to commercialize the platform, is long defunct — though others, including ROPHAI, BHealthCare, and MagicNurse, continue to work in the space. But the Rutgers proof-of-concept demonstration points toward the tantalizing possibility of fully automated blood testing at the point of care, with robots handling everything from the needle stick to the analysis.It is, in some ways, the promise Theranos made—but built on conventional, validated laboratory technology rather than the dubious science and deception that brought that particular company down.“I have no doubt that is the future,” says Gregory Retzinger, a clinical pathologist at the Northwestern University Feinberg School of Medicine in Chicago who collaborates with Vitestro and has tried the Aletta device himself. (“It was painless, it was fast,” he says.)For now, Giesen says Vitestro is keeping its ambitions—and its machine—focused on the blood collection process itself, though he believes Aletta could ultimately do far more. The company plans to launch Aletta in Europe next year, with the U.S. market to follow.