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, SEOUL

IROS 2026: 27 September–1 October 2026, PITTSBURGH

CoRL 2026: 9–12 November 2026, AUSTIN

Enjoy 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 ]

The U.S. is shutting out more foreign-made drones and robots. China’s scale means the global competition may simply move elsewhere.

The UK becomes the first country to get access to Avengers Labs, Ukraine's platform holding roughly five million annotated combat images for training military AI. Three British startups are already working on pilot projects. The deal shows how real war data is systematically becoming the currency for building out autonomous weapons tech.

The article Ukraine opens its massive labeled battlefield dataset to British firms in a landmark AI weapons partnership appeared first on The Decoder.

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, SEOUL

IROS 2026: 27 September–1 October 2026, PITTSBURGH

CoRL 2026: 9–12 November 2026, AUSTIN

Enjoy today’s videos!

This is very, very cool. But I’m trying to think of what the commercial use case will be, you know? I guess, high speed, incredibly dangerous package delivery to second-floor windows or something...?

[ Unitree ]

Humans have a remarkable ability to perform new physical skills from only one or a few examples. Our latest robot foundation model, GEN-1.5, exhibits the beginnings of that same ability: It can learn a new task in seconds, from a single example, without gradient updates or fine-tuning. It displays broad capabilities across one-shot and few-shots learning from demonstration, as well as zero-shot physical generalization. Although the tasks are simple and short-horizon, this is the first model we know for which one-shot and few-shots learning of physical skills have emerged at scale. We view these results as a significant step toward our mission of building general intelligence for the physical world.

I will make the cautionary point that for many of these “the model figured it out” tasks, the blog post can only say that there was no relevant pretraining data “to the best of our knowledge.”

[ Generalist ]

BeanBot is a robot inspired by Mexican jumping beans, and I need say no more.

[ IIT ]

As a professional bagpiper who definitely pays very close attention to whatever that annoying tapping noise is coming from the back of the band, I can attest to this group of robot drummers being absolutely top-notch.

[ AgileX Robotics ]

What does it take for an aerial robot to move through a sequence of arbitrary poses—fast, precisely, and continuously? Rather than teaching the robot a behavior from data, we asked how far a first-principles analytical model could take us. Through a collaboration between the AIMS Group at the Hong Kong Polytechnic University and DRAGON Lab at the University of Tokyo, we developed the first sequential-convex-programming-based trajectory-optimization framework for generalized multirotors, covering both conventional and omnidirectional platforms.

[ DRAGON Lab ]

Thanks, Moju!

This is a nifty idea that adapts a kind of interface frequently used for robot training and uses it for human training instead.

[ MIT ]

Gravis Robotics brings robotic intelligence to heavy construction machines. Our retrofit kit, the Gravis Rack, turns off-the-shelf hydraulic machines into robots. Cameras, lidar, and onboard compute lets your machine see and understand its surroundings, and learning-based control lets it work close to its limits, moving more dirt with full, fast cycles.

[ Gravis Robotics ]

Robust brachiation requires precise hand movements to grasp and release bars together with highly coordinated whole-body motion. To address this challenge, we propose a learning-based framework centered on waypoint-guided reinforcement learning (WGRL). WGRL guides the end effector through waypoints while allowing RL to explore and generate dynamic whole-body behaviors. With this approach, the learned policy demonstrated robust brachiation across diverse courses with different bar heights, spacings, and orientations in sim-to-sim experiments. In the real world, our life-size dual-arm robot successfully traversed four consecutive bars.

[ EVARL ]

Thanks, Ayumu!

Well, here’s a different approach to welding in shipyards with robots.

[ Kawasaki ]

We should have a lot more robots in agriculture, if only they’d lettuce.

[ Flexiv ]

We’ve all had refs like these.

[ PHYBOT ]

I got stuck after the first 15 seconds of this video trying to imagine what any of these home humanoids would usefully do if they dropped a glass.

[ Zhejiang Humanoid ]

Shakey the Robot doesn’t get enough love.

[ SRI ]

This work introduces a novel approach to physical human-robot interaction (pHRI) by leveraging the joint torque sensors of standard collaborative robots. By mounting a passive, uninstrumented plexiglass touchpad to the robot’s flange, we transform the robot into a handwriting-based input interface.

[ TS-Robotics ]

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Microspines are one of many ways to enable robots to latch onto surfaces like walls and ceilings. Now roboticists in Canada are using the mini spikes to get drones to land on a more challenging, remote surface: icebergs.

Like a spider, the Ice Dart can land on and latch onto steep, slippery surfaces such as icebergs and glaciers—an increasingly useful capability as activity in the Arctic increases. The drone can grip onto icy slopes of nearly 60 degrees, which is way beyond what most humans could manage without special equipment.

In a recent study, researchers explained how they developed the Ice Dart drone with a special landing gear that absorbs the impact of a hard landing while holding the drone in place with tiny spines that penetrate and grip the ice.

---

Published in IEEE Transactions on Field Robotics, the study describes how the Ice Dart was able to land on icebergs and a glacier in southeast Iceland. Tests took place amid persistent winds and temperatures of 0 to 10 °C along the ruggedly breathtaking Fjallsjökull (pronounced “FYATLS-yuh-kuutl”) glacier, which empties into a lagoon filled with icebergs. The drone was able to successfully perch at speeds of up to 3 meters per second and slopes of up to 58 degrees, with a success rate of 100 percent even in wind speeds of 30 km/h.

The researchers were motivated by a desire to allow drones to land almost anywhere in the world, since the availability of safe landing sites is one of the primary limitations on where and how drones can operate. The researchers already have a history of developing drones that can land on fast-moving trucks as well as trailers, boats, and steep roofs.

Ice Perching

“The ability to land rather than hover can fundamentally change how drones are used in the field,” says Alexis Lussier Desbiens, a professor of engineering at Université de Sherbrooke, in Sherbrooke, Quebec, Canada, who coauthored the study. “Once a drone has landed, energy consumption drops dramatically, allowing much longer observation periods with a small aircraft. The drone also becomes completely silent and can even reduce or eliminate its thermal and RF signature by shutting down major onboard systems.”

Landing on icebergs specifically allows drones to monitor them for days or months, producing more detailed observation than a quick aerial surveillance mission. This could simplify iceberg tracking compared to methods such as helicopter deployment, dropped instruments, or dart-like tracking devices, and provide another data layer to satellite and ship-based iceberg detection, according to the researchers. It could also provide a means of monitoring icebergs that are otherwise untrackable.

With its carbon-fiber construction, the Ice Dart drone weighs just 2.65 kg and has four legs arranged in an X shape, attached to its body with a pivot joint. Used in the group’s previous drone research, this landing gear disperses energy to reduce impact and overcomes multiple engineering challenges. The friction shock absorbers consist of 38 disks that generate friction torque as the legs move up and down upon impact. This lowers the UAV’s center of mass and helps spread out the kinetic energy of landing, but the real trick comes in the form of two retractable spines on each foot—one for uphill and one for downhill grip. The larger spine engages on the more heavily loaded downhill feet, and the smaller, thinner spine engages more easily on the uphill feet, even under very low loads on steep slopes. The spines only penetrate the ice as the suspension compresses, generating grip and protecting them from high-impact forces.

“The inspiration for the retractable spines in the feet came from looking at a cat’s claws and their ability to deploy only when needed,” says Isaac Tunney, a Université de Sherbrooke postdoc in mechanical and robotics engineering who was lead author of the paper. “I wanted to create feet that would naturally and passively engage their spines in the ice at the right moment, regardless of the drone’s orientation, the surface geometry, or the ice conditions.”

Arctic Surveillance

William D. Harcourt is a researcher at the University of Aberdeen, in Aberdeen, Scotland, focused on Arctic glaciers, snow, and sea ice, as well as the use of remote sensing and machine learning techniques. Harcourt was not involved in the study, but he sees several potentially interesting applications of the technology.

“Near the front of tidewater glaciers, these systems could enable measurement of stress and strain and help us understand calving processes,” Harcourt says. “Drones can be used as a mobile GPS, literally acting as a receiver on the ice, but the system would need to solve tilting issues as 3D change measurements usually required the antenna to be horizontal. However, if these problems can be solved, it could be used to track iceberg movements.”

The researchers want to continue developing the Ice Dart technology for real-world applications, including autonomous landing site selection and an emergency takeoff capability to be used if an iceberg rolls over or breaks apart. This August, the drone will be deployed during a Canadian Arctic mission to land on icebergs, collect data, and help validate ship-based iceberg-detection systems.

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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

Speaking from experience, I can tell you that the best part of any DARPA challenge is when things go horribly wrong. And after you enjoy all the crashes (followed by all of the battery fires), get caught up with the DARPA Lift Challenge with video recaps of the final few days.

[ DARPA Lift Challenge ]

Drone delivery: coming soon to a moving vehicle (or perhaps even through an open window) near you.

[ HKUST Aerial Robotics Group ]

This tiny little robot called STEMbot (as in stem, not STEM) can climb up and around plant stems to check for pests. It’s not very fast, but it sure is adorable.

[ STEMbot ]

Monumental’s robots delivered the brickwork for a semi-detached home, laying around 20,000 bricks in a new community.

[ Monumental ]

Meet the world’s most “truss’t-worthy” robot.

[ Modlab University of Pennsylvania ]

Stanford BDML and Honeybee Robotics propose a payload to test gecko-inspired adhesives in spaaace!

[ NASA ]

How can a legged robot organize its own walking while maintaining a desired direction? In this work, we present a Differential Adaptive Steering (DAST) mechanism for directional adaptation in legged robots under decentralized adaptive control.

[ BRAIN VISTEC ]

I do not care even a little bit if a robot fails (safely, of course), as long as it recovers from that failure.

[ Sanctuary AI ]

Even for a robot that doesn’t drink champagne, those are some pretty light pours.

[ Kawasaki Robotics ]

If we as a society would just accept that the appropriate place to store clothing is in a pile on the floor, robots would have a much easier time of it.

[ LimX Dynamics ]

To be fair, this is also the speed at which I fold shirts.

[ Sharpa ]

Our DR02 humanoid robot takes on the stairs with stable, controlled movement—steady steps, steady progress.

[ DEEP Robotics ]

Two words: structural minifridge. Or is it mini fridge...? Whatever, THREE words.

[ AgileX ]

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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

The DARPA Lift Challenge is taking place through this weekend. There are a couple of very brief overview videos from the past couple of days, which are only really interesting because they give you a quick look at some utterly bizarre heavy-lift drone designs. If you like what you see, DARPA has recorded livestreams of the entire event so far. We’ve posted one of those at the end of this section, and if you want to be impressed by some super-weird drones, check out this and this.

[ DARPA Lift Challenge ]

When NASA’s SkyFall helicopters take to the Martian skies, one of their tasks will be to hunt for frozen water—a critical resource for future astronauts—using ground-penetrating radar. For that radar to work, the rotorcraft will carry a flexible, fabric-based antenna that extends below the aircraft without interfering with landings or breaking at touchdown.

[ NASA ]

Why would you even want a five-fingered humanoid hand when you could have something so much better?

[ Flexiv ]

We’ve improved how GEN-1 learns to adapt to new actuators and new robots at the lowest level, with up to 10-20x gains on internal benchmarks. This significantly boosts performance on high-precision tasks like disassembling parts from a NIST board.

[ Generalist ]

This is certainly one of the best-looking humanoid robots out there.

[ Generative Bionics ]

A little on the technical side, but the concept here is important, I think: being able to control an assistive robot through touch.

[ Tac-Nav ]

We present SonicFly, a passive aeroacoustic perception framework that enables one unmanned aerial vehicle (UAV) to estimate and follow another using only the leader’s intrinsic flight sound.

[ General Robotics Lab ]

Okay, but... Get a job?

[ ROBOTIS ]

The British Antarctic Survey says autonomous vehicles are ‘transforming polar science’ A robot is cruising beneath Antarctic sea ice. Another is bolted to the face of a collapsing Greenland glacier. A third is silently counting penguins on a remote, windswept beach. None of them has a human operator nearby. Across the Arctic and Antarctic, autonomous […]

Newly developed control method overcomes unexpected movements, improving disturbance rejection and reducing X-axis position error by 53.1 percent Small flapping-wing (FW) robots could support applications such as inspection and search and rescue. However, they are highly susceptible to disturbances such as wind gusts, making stable flight challenging. Researchers from Japan’s Chiba University investigated the flight […]

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.

Actuate 2026: 18–19 August 2026, SAN FRANCISCO

IROS 2026: 27 September–1 October 2026, PITTSBURGH

Humanoids Summit Seoul: 22–23 September 2026, SEOUL

Enjoy today’s videos!

Introducing Gemini Robotics 2—the intelligence layer powering the next generation of truly adaptable robots. As it takes its first literal steps, this major advance unlocks intelligent whole-body control, advanced dexterity, and multirobot collaboration.

[ Google DeepMind ]

THE ROADMAP! NOOOOO!

[ Agility ]

Videos like this always make me wonder how repairable these robots are. Very, I would hope.

[ Unitree ]

Humans routinely communicate through abstractions of their bodies, including shadows, silhouettes, and reflections. Here, we present a robotic system capable of dynamic shadow expression using a 21-degrees-of-freedom dexterous hand with compliant soft skin and a learned shadow self-model.

[ General Robotics Lab ]

Human-to-quadruped motion transfer is an odd concept, but I’m here for it.

[ Disney Research ]

Meet Stretch 4.0—the one-armed, three-wheeled robot that can navigate your home safely. Would you rather a humanoid robot or Stretch?

[ Hello Robot ]

And now, this, for some reason.

[ PNDbotics ]

I’m not sure we’re allowed to be impressed if you resize a badminton court to accommodate your robot.

[ PHYBOT ]

Golden eagles care not for drones.

[ Team BlackSheep ]

University of Southern California researchers work with NASA and others to train robot dogs for planetary exploration on Mars, the moon, and beyond!

[ Research in Applied Decisions: RAD Lab ]

Thanks, Cristina!

WABOT-1 was arguably the birth of the humanoid robot in Japan. We’ve come a long way, and it’s good to be reminded where we started.

[ Takanishi Lab ]

If only this video was at 1x instead of 5x we could have had 15 hours of Memo folding laundry.

[ Sunday Robotics ]

Flytrex, an autonomous drone food delivery service, and Nash, the autonomic logistics platform, have announced a partnership to power an autonomous-first, multi-modal delivery platform across Flytrex’s operations. Now live across all of Flytrex’s Dallas-Fort Worth sites, the integration brings Flytrex’s autonomous fleet together with Nash’s orchestration layer, so every customer order is routed to the […]

Delivery drone company Wing and retail giant Walmart have launched a drone delivery service in Greater Orlando, marking Wing’s first operations in Florida and extending the companies’ growing US drone delivery network. The service enables customers to order thousands of everyday items from participating Walmart stores, including groceries, household products, electronics, school supplies and health […]

There are many words that I would never, ever use to describe a drone. Stealthy. Subtle. Whatever the opposite of obnoxious is. Much of this is because of the giant angry bee sound that drones tend to make, but it’s also the way that they look in flight: With uncannily linear movements and an even less canny ability to hover perfectly still, they tend to draw the eye as affronts to nature.

In a paper presented this week at Robotics Science and Systems 2026 in Sydney, roboticists from Northwestern University, Evanston, Ill., demonstrated a drone called Phantom Twist that is essentially invisible to humans, being an order of magnitude more difficult to see in flight than a typical quadrotor. They accomplished this with the aid of computational design, and while the resulting hardware is, I would argue, also an order of magnitude more of an affront to nature than a typical quadrotor represents, it’s pretty amazing how well it works.

Phantom Twist spins so fast, it’s practically invisible.Michael Rubenstein/Northwestern University

The trick here is easy to see, even if the drone isn’t. By spinning in flight at between 15 and 25 hertz, Phantom Twist takes advantage of humans’ decidedly mediocre visual system to turn a solid spinning object into an opaque smear. Human eyes take some amount of time (typically about 100 milliseconds) to integrate what we see before sending the full scene off to our brains for processing. Moving objects can cause problems for this system, because if the movement is fast enough, our eyes are forced to average that motion across the scene, combining it with whatever is in the background and resulting in a transparent blur. This effect is called persistence of vision. For something that spins like Phantom Twist, that motion blur comes from the drone’s rapid rotation, and it works because most of the drone is cleverly designed to be empty space.

Drones that spin in flight are nothing new—we’ve covered a bunch of them in the past, including Picolissimo and any number of samara drones inspired by the spinning flight of maple seeds. What makes Phantom Twist unique, and also very odd, is that the design was computationally optimized for low visibility.

Controlling how drones like this fly

Before we get into that, though, a quick note about how drones like this can even fly controllably, because it’s not at all obvious. With just a single motor and no control surfaces, the only possible control input is through the motor itself, and by pulsing the motor speed up or down at just the right time during each rotation, the drone can translate in any direction. Altitude control comes from changing overall motor thrust, and the drone‘s spinning nature makes it passively stable.

Carbon fiber rods connect batteries, a controller, some counterweights, and a motor and propeller. The research robot also includes optical tracking tags.Michael Rubenstein/Northwestern University

The bits that you need for this kind of drone include the motor and propeller, a couple of batteries, a controller, some counterweights (which could be replaced with more batteries or payload), 0.8-mm carbon fiber rods to tie it all together, and a connector for the handheld launcher that gets the whole thing up to speed. The actual arrangement of these components is surprisingly flexible, and that’s where the invisibility comes in.

“The design space is high dimensional,” explains Northwestern’s Michael Rubenstein. “It’s very difficult for a human to reason through all the trade-offs between the physical constraints required for stable flight and the visual appearance of the spinning drone, and I don’t think we would have easily arrived at this low-visibility design ourselves.”

The visibility (or not) of Phantom Twist is primarily driven by the extent to which different components line up with each other from the perspective of someone looking at the drone. The more components that line up with each other as the drone flies, the less background you see through the spinning drone, and the more visible the drone becomes. Because you might be looking at the drone from a number of different angles, and also because the drone has to be stable enough for controlled flight, there are a bunch of different things that need to be optimized all at once, which is why computational design is effective here.

Phantom Twist’s final design was generated using an iterative optimizer which had a goal of minimizing a metric called learned perceptual image patch similarity, or LPIPS, while making sure that the design could still physically work. LPIPS is the difference between two images: a background image, and a background image with an overlay of the simulated spinning drone. The smaller that difference is, the more invisible that design is. It’s tricky for a human to consider all of the variables at once, but Rubenstein says that the final design does make intuitive sense, because “the automated pipeline prefers placements where components don’t visually overlap as it spins, or where the components are too close to the center of rotation.”

Two iterations of Phantom Twist drones are shown with their handheld launching mechanisms. The better-optimized version [bottom row] relocates the launcher interface to remove components that are too close to the central axis, making them more visible.Michael Rubenstein/Northwestern University

Out of a starting set of around 20,000 feasible Phantom Twist configurations, the optimized design (the one that you see or don’t see in the pictures and videos) has a LPIPS score of 0.0104. A human-designed Phantom Twist is about twice as visible, with a LPIPS score of around 0.2, and a conventional quadrotor (of the same size) would be over 10 times more visible. And there’s still a bit more optimization that could be done with the electrical wiring as well as increasing the baseline transparency of the components themselves.

Phantom Twist is currently controlled using an optical tracking system, which means that it’s not yet capable of flying outside of a controlled environment. But Rubenstein has built other drones along similar principles in the past, which have successfully flown outside, and he’s optimistic about using those techniques to break Phantom Twist out of the lab. The spinning behavior might even enable some useful sensing capabilities, he says. “An interesting possibility is mounting a camera on the spinning body. As the vehicle rotates, it could capture imagery in every direction, effectively creating a 360-degree view of its surroundings that could be used for onboard navigation and control.”

As for what a drone like Phantom Twist could be used for—assuming that the sound can be mitigated somewhat (and there are potential approaches to making that happen), a stealthy microdrone could do all sorts of things with covert surveillance being the most obvious application. For his part, Rubenstein says that he’s personally excited about the potential for watching wildlife, “where a less-intrusive drone could observe animals while minimizing its impact on their natural behavior.” The elephants in particular would certainly appreciate that.

For a deeper dive into all the particulars of this project, read the paper: Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric, by Jingxian Wang, Chen Yu, David Matthews, Emma Alexander, Sam Kriegman, and Michael Rubenstein from Northwestern University, which is being presented this week at RSS 2026 in Sydney.

ZTASP is a mission-scale assurance and governance platform designed for autonomous systems operating in real-world environments. It integrates heterogeneous systems—including drones, robots, sensors, and human operators—into a unified zero-trust architecture. Through Secure Runtime Assurance (SRTA) and Secure Spatio-Temporal Reasoning (SSTR), ZTASP continuously verifies system integrity, enforces safety constraints, and enables resilient operation even under degraded conditions.

ZTASP has progressed beyond conceptual design, with operational validation at Technology Readiness Level (TRL) 7 in mission critical environments. Core components, including Saluki secure flight controllers, have reached TRL8 and are deployed in customer systems. While initially developed for high-consequence mission environments, the same assurance challenges are increasingly present across domains such as healthcare, transportation, and critical infrastructure.

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