Despite the recent deluge of videos of humanoid robots doing backflips and kung-fu, the actual pace of progress towards humanoids that can do economically viable jobs at scale has at times seemed a bit plodding. There are some good reasons for this: a humanoid robot needs to be powerful enough to do useful work, safe enough for humans to walk past, and have enough battery life to limit how much of the day it’s hooked to a charger. But all three of those necessary features actively work against each other.
With its new Digit 5 robot, announced today, Agility Robotics may have found the sweet spot. Digit 5 is a humanoid worker that can lift 23 kilograms up 2.1 meters, can work in close proximity to people without relying on physical barriers, and can operate for more than 20 hours a day. This combination of power, safety, and battery life has resulted in a 1.8 m tall, 129 kg robot that prioritizes functionality over style and pretty much everything else, but this is exactly what it’s going to take to get humanoids a job.
Humanoid robot companies have been reluctant to talk about safety, because there are no easy answers to “what happens if your fundamentally unstable bipedal robot falls over onto me/my pet/my baby?” And there are still no easy answers, but Agility does have an answer that allows Digit to be verifiably safe. That answer is to make sure that it’s physically impossible for Digit to fall over on anyone, ever, by “autonomously avoiding, stopping or assuming a seated position,” according to today’s press release. In other words, as a person approaches Digit, Digit will put down whatever it’s carrying and then if necessary make sure that it’s stably seated on the ground before that person gets near. It may not be elegant, but it works, which is more than can be said for any other commercial humanoid that I’m aware of.
The other big change is Digit’s legs. Somewhat famously, Agility robotics introduced first Cassie and then a whole series of Digit robots featuring bird-like ‘backwards’ legs. In his 2019 article for IEEE Spectrum, Agility co-founder Jonathan Hurst explained that this leg configuration was the result of a careful analysis of the physics of animal locomotion, rather than for the leg to look like any sort of animal in particular. The unfortunate reality for those of us who were fans of Cassie is that bird legs are optimal for dynamic motion, while human legs are better for squats and lifts, which will make sense to anyone who has seen an ostrich trying to lift a heavy box up off the floor and also anyone who has tried to outrun a cassowary.
How much will Digit 5 cost?
We also know a couple of things about Digit 5 that weren’t included in today’s announcement, thanks to Agility’s June SEC filing in advance of their plan to go public by the end of 2026. For example, at launch, Digit 5’s BOM (bill of materials) cost is likely to be somewhere between $150,000 and $200,000. This is just the cost of the parts that make up a Digit robot, not what it costs for Agility to actually build one. Based on real production data, Agility is anticipating that with some near-term optimizing and at a volume of 10,000 units per year, that cost should drop to under $50,000 per robot.
In this rendering, Digit 5 robots work in a factory with humans nearby, no safety barriers needed. When a human approaches, the robot puts down what it’s carrying and squats down so it can’t fall on anybody.Agility
Arguably less important than the per-robot cost is how Agility (and its customers) will profit from Digit 5. In the filing, Agility estimates (using “rounded estimates” which are “purely illustrative”) that Digit 5 robots will be offered to customers as a service at something like $8,500 per month. Based on 20 hours per day of work, and assuming that the total cost of a human worker to their employer is $30.50/hr, Digit 5 as a service could save employers $100,000 per year, per robot.
It’s important to note that all of these numbers are just estimates, and that all kinds of things (many of them not under Agility’s control) could cause them to change. They’re most useful as an illustration of Agility’s broad approach to making Digit 5 profitable. It’s also important to note the assumption here with such a direct comparison is that Digit 5 will be a more or less effortless drop-in replacement for human labor, which is not something that I’m entirely sure has ever happened with any robot, anywhere.
Where does Digit 5 go from here? Today’s press release says that “as of May 2026, Agility had more than $300 million in multi-year customer orders for Digit 5, with a sales pipeline of prospective customers across manufacturing, warehousing, and logistics.” That works out to comfortably under 1,000 robots, one-tenth of the full capacity of Agility’s RoboFab factory in Oregon.
Between now and all those robots, however, lies an unpredictable entry into the stock market through what’s called a SPAC merger. That’s expected to close within the next few months. Agility hopes to raise more than $620 million through the merger, and it will primarily spend the money to scale production of Digit 5, and get it to customers. Those in the EU and the UK should be able to buy the bots in 2027.
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.
Stabilizing unsecured payloads against the inherent oscillations of dynamic bipedal locomotion remains a critical engineering bottleneck for humanoids in unstructured environments. To solve this, we introduce ReST-RL, a hierarchical reinforcement-learning architecture that explicitly decouples locomotion from payload stabilization. Successfully deployed on the Unitree G1 humanoid hardware, this modular approach demonstrates highly reliable zero-shot sim-to-real generalization across various objects and external force disturbances.
Online, humanoid robots are very impressive to watch, but behind the scenes, most of those movements are carefully choreographed. Researchers in Carnegie Mellon University’s Safe AI Lab are instead teaching robots how to adapt. Their system, called APEX, allows a humanoid robot to navigate obstacles using adaptive, full-body maneuvers.
Researchers from North Carolina State University have created teardrop-shaped soft robots that leap upward or forward when exposed to infrared light—and will keep jumping as long as the light is present.
The robots are made of a liquid-crystal elastomer ribbon shaped like a teardrop, with a thin aluminum tube shaped like a V at one end. When exposed to light from an infrared lamp, the surface of the ribbon contracts, causing the ribbon to rotate. The stiff V at one end of the robot prevents the ribbon from simply rolling in place, causing the ribbon to twist tighter and tighter. This stores energy until the twist reaches a critical point when the ribbon releases that energy, causing the V at one end of the teardrop to snap downward and strike the surface. This launches the teardrop into the air.
I’ll be honest—I was prepared to be underwhelmed by the DARPA Lift Challenge, but there was such creativity in the heavy-lift drone designs that I’m excited for it to come back in 2028.
Humans use not only muscle signals but also stretched skin around joints as a cue for proprioception. To mimic this biological mechanism, we developed a three-layer joint-covering skin with 44 pressure- and stretch-sensitive elements for the musculoskeletal humanoid Musashi-W.
In Turpan, China—known as the City of Fire—summer ground temperatures can exceed 50 °C. During the grape harvest, farmers traditionally carry heavy baskets back and forth under the intense heat, while every extra minute in the sun can affect the freshness of the fruit. This year, the DEEP Robotics Lynx M20S joined the harvest.
This video showcases the achievements of the first OH! GYM! Project cohort, a group of university and graduate students who explored, developed, and deployed their own humanoid behaviors using the open-source AI Sapiens K1 platform. Over the course of one month, the students experienced the complete process of humanoid development—from creating motions in simulation to transferring them onto a physical robot through repeated Sim2Real experiments.
Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value.
In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality.
The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe.
The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough.
As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics.
1. The YouTube-to-Reality Gap Is Real
For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon.
Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work.
2. Data Is An Unsolved Challenge
Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training.
The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics
Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world.
Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level.
3. There Will Be No Single Robot AI
We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us.
General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time.
We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models.
AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies.
4. Hardware Is Still Very Hard
Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators.
Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things.
Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people.
5. Real Value Comes From “Easy” Tasks
There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots.
Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control.
Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models.
This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience.
AI Robots Are Coming, One Step at a Time
As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.
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