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#439773 How the U.S. Army Is Turning Robots Into ...
This article is part of our special report on AI, “The Great AI Reckoning.”
“I should probably not be standing this close,” I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.
The robot, named
RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to “go clear a path.” It's then up to the robot to make all the decisions necessary to achieve that objective.
The ability to make decisions autonomously is not just what makes robots useful, it's what makes robots
robots. We value robots for their ability to sense what's going on around them, make decisions based on that information, and then take useful actions without our input. In the past, robotic decision making followed highly structured rules—if you sense this, then do that. In structured environments like factories, this works well enough. But in chaotic, unfamiliar, or poorly defined settings, reliance on rules makes robots notoriously bad at dealing with anything that could not be precisely predicted and planned for in advance.
RoMan, along with many other robots including home vacuums, drones, and autonomous cars, handles the challenges of semistructured environments through artificial neural networks—a computing approach that loosely mimics the structure of neurons in biological brains. About a decade ago, artificial neural networks began to be applied to a wide variety of semistructured data that had previously been very difficult for computers running rules-based programming (generally referred to as symbolic reasoning) to interpret. Rather than recognizing specific data structures, an artificial neural network is able to recognize data patterns, identifying novel data that are similar (but not identical) to data that the network has encountered before. Indeed, part of the appeal of artificial neural networks is that they are trained by example, by letting the network ingest annotated data and learn its own system of pattern recognition. For neural networks with multiple layers of abstraction, this technique is called deep learning.
Even though humans are typically involved in the training process, and even though artificial neural networks were inspired by the neural networks in human brains, the kind of pattern recognition a deep learning system does is fundamentally different from the way humans see the world. It's often nearly impossible to understand the relationship between the data input into the system and the interpretation of the data that the system outputs. And that difference—the “black box” opacity of deep learning—poses a potential problem for robots like RoMan and for the Army Research Lab.
In chaotic, unfamiliar, or poorly defined settings, reliance on rules makes robots notoriously bad at dealing with anything that could not be precisely predicted and planned for in advance.
This opacity means that robots that rely on deep learning have to be used carefully. A deep-learning system is good at recognizing patterns, but lacks the world understanding that a human typically uses to make decisions, which is why such systems do best when their applications are well defined and narrow in scope. “When you have well-structured inputs and outputs, and you can encapsulate your problem in that kind of relationship, I think deep learning does very well,” says
Tom Howard, who directs the University of Rochester's Robotics and Artificial Intelligence Laboratory and has developed natural-language interaction algorithms for RoMan and other ground robots. “The question when programming an intelligent robot is, at what practical size do those deep-learning building blocks exist?” Howard explains that when you apply deep learning to higher-level problems, the number of possible inputs becomes very large, and solving problems at that scale can be challenging. And the potential consequences of unexpected or unexplainable behavior are much more significant when that behavior is manifested through a 170-kilogram two-armed military robot.
After a couple of minutes, RoMan hasn't moved—it's still sitting there, pondering the tree branch, arms poised like a praying mantis. For the last 10 years, the Army Research Lab's Robotics Collaborative Technology Alliance (RCTA) has been working with roboticists from Carnegie Mellon University, Florida State University, General Dynamics Land Systems, JPL, MIT, QinetiQ North America, University of Central Florida, the University of Pennsylvania, and other top research institutions to develop robot autonomy for use in future ground-combat vehicles. RoMan is one part of that process.
The “go clear a path” task that RoMan is slowly thinking through is difficult for a robot because the task is so abstract. RoMan needs to identify objects that might be blocking the path, reason about the physical properties of those objects, figure out how to grasp them and what kind of manipulation technique might be best to apply (like pushing, pulling, or lifting), and then make it happen. That's a lot of steps and a lot of unknowns for a robot with a limited understanding of the world.
This limited understanding is where the ARL robots begin to differ from other robots that rely on deep learning, says Ethan Stump, chief scientist of the AI for Maneuver and Mobility program at ARL. “The Army can be called upon to operate basically anywhere in the world. We do not have a mechanism for collecting data in all the different domains in which we might be operating. We may be deployed to some unknown forest on the other side of the world, but we'll be expected to perform just as well as we would in our own backyard,” he says. Most deep-learning systems function reliably only within the domains and environments in which they've been trained. Even if the domain is something like “every drivable road in San Francisco,” the robot will do fine, because that's a data set that has already been collected. But, Stump says, that's not an option for the military. If an Army deep-learning system doesn't perform well, they can't simply solve the problem by collecting more data.
ARL's robots also need to have a broad awareness of what they're doing. “In a standard operations order for a mission, you have goals, constraints, a paragraph on the commander's intent—basically a narrative of the purpose of the mission—which provides contextual info that humans can interpret and gives them the structure for when they need to make decisions and when they need to improvise,” Stump explains. In other words, RoMan may need to clear a path quickly, or it may need to clear a path quietly, depending on the mission's broader objectives. That's a big ask for even the most advanced robot. “I can't think of a deep-learning approach that can deal with this kind of information,” Stump says.
Robots at the Army Research Lab test autonomous navigation techniques in rough terrain [top, middle] with the goal of being able to keep up with their human teammates. ARL is also developing robots with manipulation capabilities [bottom] that can interact with objects so that humans don't have to.Evan Ackerman
While I watch, RoMan is reset for a second try at branch removal. ARL's approach to autonomy is modular, where deep learning is combined with other techniques, and the robot is helping ARL figure out which tasks are appropriate for which techniques. At the moment, RoMan is testing two different ways of identifying objects from 3D sensor data: UPenn's approach is deep-learning-based, while Carnegie Mellon is using a method called perception through search, which relies on a more traditional database of 3D models. Perception through search works only if you know exactly which objects you're looking for in advance, but training is much faster since you need only a single model per object. It can also be more accurate when perception of the object is difficult—if the object is partially hidden or upside-down, for example. ARL is testing these strategies to determine which is the most versatile and effective, letting them run simultaneously and compete against each other.
Perception is one of the things that deep learning tends to excel at. “The computer vision community has made crazy progress using deep learning for this stuff,” says Maggie Wigness, a computer scientist at ARL. “We've had good success with some of these models that were trained in one environment generalizing to a new environment, and we intend to keep using deep learning for these sorts of tasks, because it's the state of the art.”
ARL's modular approach might combine several techniques in ways that leverage their particular strengths. For example, a perception system that uses deep-learning-based vision to classify terrain could work alongside an autonomous driving system based on an approach called inverse reinforcement learning, where the model can rapidly be created or refined by observations from human soldiers. Traditional reinforcement learning optimizes a solution based on established reward functions, and is often applied when you're not necessarily sure what optimal behavior looks like. This is less of a concern for the Army, which can generally assume that well-trained humans will be nearby to show a robot the right way to do things. “When we deploy these robots, things can change very quickly,” Wigness says. “So we wanted a technique where we could have a soldier intervene, and with just a few examples from a user in the field, we can update the system if we need a new behavior.” A deep-learning technique would require “a lot more data and time,” she says.
It's not just data-sparse problems and fast adaptation that deep learning struggles with. There are also questions of robustness, explainability, and safety. “These questions aren't unique to the military,” says Stump, “but it's especially important when we're talking about systems that may incorporate lethality.” To be clear, ARL is not currently working on lethal autonomous weapons systems, but the lab is helping to lay the groundwork for autonomous systems in the U.S. military more broadly, which means considering ways in which such systems may be used in the future.
The requirements of a deep network are to a large extent misaligned with the requirements of an Army mission, and that's a problem.
Safety is an obvious priority, and yet there isn't a clear way of making a deep-learning system verifiably safe, according to Stump. “Doing deep learning with safety constraints is a major research effort. It's hard to add those constraints into the system, because you don't know where the constraints already in the system came from. So when the mission changes, or the context changes, it's hard to deal with that. It's not even a data question; it's an architecture question.” ARL's modular architecture, whether it's a perception module that uses deep learning or an autonomous driving module that uses inverse reinforcement learning or something else, can form parts of a broader autonomous system that incorporates the kinds of safety and adaptability that the military requires. Other modules in the system can operate at a higher level, using different techniques that are more verifiable or explainable and that can step in to protect the overall system from adverse unpredictable behaviors. “If other information comes in and changes what we need to do, there's a hierarchy there,” Stump says. “It all happens in a rational way.”
Nicholas Roy, who leads the Robust Robotics Group at MIT and describes himself as “somewhat of a rabble-rouser” due to his skepticism of some of the claims made about the power of deep learning, agrees with the ARL roboticists that deep-learning approaches often can't handle the kinds of challenges that the Army has to be prepared for. “The Army is always entering new environments, and the adversary is always going to be trying to change the environment so that the training process the robots went through simply won't match what they're seeing,” Roy says. “So the requirements of a deep network are to a large extent misaligned with the requirements of an Army mission, and that's a problem.”
Roy, who has worked on abstract reasoning for ground robots as part of the RCTA, emphasizes that deep learning is a useful technology when applied to problems with clear functional relationships, but when you start looking at abstract concepts, it's not clear whether deep learning is a viable approach. “I'm very interested in finding how neural networks and deep learning could be assembled in a way that supports higher-level reasoning,” Roy says. “I think it comes down to the notion of combining multiple low-level neural networks to express higher level concepts, and I do not believe that we understand how to do that yet.” Roy gives the example of using two separate neural networks, one to detect objects that are cars and the other to detect objects that are red. It's harder to combine those two networks into one larger network that detects red cars than it would be if you were using a symbolic reasoning system based on structured rules with logical relationships. “Lots of people are working on this, but I haven't seen a real success that drives abstract reasoning of this kind.”
For the foreseeable future, ARL is making sure that its autonomous systems are safe and robust by keeping humans around for both higher-level reasoning and occasional low-level advice. Humans might not be directly in the loop at all times, but the idea is that humans and robots are more effective when working together as a team. When the most recent phase of the Robotics Collaborative Technology Alliance program began in 2009, Stump says, “we'd already had many years of being in Iraq and Afghanistan, where robots were often used as tools. We've been trying to figure out what we can do to transition robots from tools to acting more as teammates within the squad.”
RoMan gets a little bit of help when a human supervisor points out a region of the branch where grasping might be most effective. The robot doesn't have any fundamental knowledge about what a tree branch actually is, and this lack of world knowledge (what we think of as common sense) is a fundamental problem with autonomous systems of all kinds. Having a human leverage our vast experience into a small amount of guidance can make RoMan's job much easier. And indeed, this time RoMan manages to successfully grasp the branch and noisily haul it across the room.
Turning a robot into a good teammate can be difficult, because it can be tricky to find the right amount of autonomy. Too little and it would take most or all of the focus of one human to manage one robot, which may be appropriate in special situations like explosive-ordnance disposal but is otherwise not efficient. Too much autonomy and you'd start to have issues with trust, safety, and explainability.
“I think the level that we're looking for here is for robots to operate on the level of working dogs,” explains Stump. “They understand exactly what we need them to do in limited circumstances, they have a small amount of flexibility and creativity if they are faced with novel circumstances, but we don't expect them to do creative problem-solving. And if they need help, they fall back on us.”
RoMan is not likely to find itself out in the field on a mission anytime soon, even as part of a team with humans. It's very much a research platform. But the software being developed for RoMan and other robots at ARL, called Adaptive Planner Parameter Learning (APPL), will likely be used first in autonomous driving, and later in more complex robotic systems that could include mobile manipulators like RoMan. APPL combines different machine-learning techniques (including inverse reinforcement learning and deep learning) arranged hierarchically underneath classical autonomous navigation systems. That allows high-level goals and constraints to be applied on top of lower-level programming. Humans can use teleoperated demonstrations, corrective interventions, and evaluative feedback to help robots adjust to new environments, while the robots can use unsupervised reinforcement learning to adjust their behavior parameters on the fly. The result is an autonomy system that can enjoy many of the benefits of machine learning, while also providing the kind of safety and explainability that the Army needs. With APPL, a learning-based system like RoMan can operate in predictable ways even under uncertainty, falling back on human tuning or human demonstration if it ends up in an environment that's too different from what it trained on.
It's tempting to look at the rapid progress of commercial and industrial autonomous systems (autonomous cars being just one example) and wonder why the Army seems to be somewhat behind the state of the art. But as Stump finds himself having to explain to Army generals, when it comes to autonomous systems, “there are lots of hard problems, but industry's hard problems are different from the Army's hard problems.” The Army doesn't have the luxury of operating its robots in structured environments with lots of data, which is why ARL has put so much effort into APPL, and into maintaining a place for humans. Going forward, humans are likely to remain a key part of the autonomous framework that ARL is developing. “That's what we're trying to build with our robotics systems,” Stump says. “That's our bumper sticker: 'From tools to teammates.' ”
This article appears in the October 2021 print issue as “Deep Learning Goes to Boot Camp.”
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Posted in Human Robots
#439662 An Army of Grain-harvesting Robots ...
The field of automated precision agriculture is based on one concept—autonomous driving technologies that guide vehicles through GPS navigation. Fifteen years ago, when high-accuracy GPS became available for civilian use, farmers thought things would be simple: Put a GPS receiver station at the edge of the field, configure a route for a tractor or a combine harvester, and off you go, dear robot!
Practice has shown, however, that this kind of carefree field cultivation is inefficient and dangerous. It works only in ideal fields, which are almost never encountered in real life. If there's a log or a rock in the field, or a couple of village paramours dozing in the rye under the sun, the tractor will run right over them. And not all countries have reliable satellite coverage—in agricultural markets like Kazakhstan, coverage can be unstable. This is why, if you want safe and efficient farming, you need to equip your vehicle with sensors and an artificial intelligence that can see and understand its surroundings instead of blindly following GPS navigation instructions.
The Cognitive Agro Pilot system lets a human operator focus on harvesting rather than driving. An integrated display and control system in the cab handles driving based on a video feed from a single low-resolution camera, no GPS or Internet connectivity required. Cognitive Pilot
You might think that GPS navigation is ideal for automated agriculture, since the task facing the operator of a farm vehicle like a combine harvester is simply to drive around the field in a serpentine pattern, mowing down all the wheat or whatever crop it is filled with. But reality is far different. There are hundreds of things operators must watch even as they keep their eyes fastened to the edge of the field to ensure that they move alongside it with fine precision. An agricultural combine is not dissimilar to a church organ in terms of its operational complexity. When a combine operator works with an assistant, one of them steers along the crop edge, while the other controls the reel, the fan, the threshing drum, and the harvesting process in general. In Soviet times, there were two operators in a combine crew, but now there is only one. This means choosing between safe driving and efficient harvesting. And since you can't harvest grain without moving, driving becomes the top priority, and the efficiency of the harvesting process tends to suffer.
Harvesting efficiency is especially important in Eastern Europe, where farming is high risk and there is only one harvest a year. The season starts in March and farmers don't rest until the autumn, when they have only two weeks to harvest the crops. If something goes wrong, every day they miss may lead to a loss of 10 percent of the yield. If a driver does a poor job of harvesting or gets drunk and crashes the machine, precious time is lost—hours or even days. About 90 percent of the combine operator's time is spent making sure that the combine is driving exactly along the edge of the unharvested crop to maximize efficiency without missing any of the crop. But this is the most unpleasant part of the driving, and due to fatigue at the end of the shift, operators typically leave nearly a meter at the edge of each row uncut. These steering errors account for a 25 percent overall increase in harvesting time. Our technology allows combine operators to delegate the driving so that they can instead focus on optimizing harvesting quality.
Add to this the fact that the skilled combine operator is a dying breed. Professional education has declined, and the young people joining the labor force aren't up to the same standard. Though the same can be said of most manual trades, this effect creates a great demand for our robotic system, the Cognitive Agro Pilot.
Developing AI systems is in my genome. My father, Anatoly Uskov, was on the first team of AI program developers at the
System Research Institute of the Russian Academy of Sciences. Their program, named Kaissa, became the world computer chess champion in 1974. Two decades later, after the collapse of the Soviet Union, the Systems Research Institute's AI laboratories formed the foundation of my company, Cognitive Technologies. Our first business was developing optical character recognition software used by companies including HP, Oracle, and Samsung, and our success allowed us to support an R&D team of mathematicians and programmers conducting fundamental research in the field of computer vision and adjacent areas.
In 2012, we added a group of mathematicians developing neural networks. Later that year, this group proudly introduced me to their creation: Vasya, a football-playing toy car with a camera for an eye. “One-eyed Vasya” could recognize a ball among other objects in our long office hallway, and push it around. The robot was a massive distraction for everyone working on that floor, as employees went out into the hallway and started “testing” the car by tripping it up and blocking its way to the ball with obstacles. Meanwhile, the algorithm showed stable performance. Politely swerving around obstacles, the car kept on looking for the ball and pushing it. It almost gave an impression of a living creature, and this was our “eureka” moment—why don't we try doing the same with something larger and more useful?
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A combine driven by the Cognitive Agro Pilot harvests grain while a human supervises from the driver's seat.Cognitive Pilot
After initially experimenting with large heavy-duty trucks, we realized that the agricultural sector doesn't have the major legal and regulatory constraints that road transport has in Russia and elsewhere. Since our priority was to develop a commercially viable product, we set up a business unit called
Cognitive Pilot that develops add-on autonomy for combine harvesters, which are the machines used to harvest the vast majority of grain crops (including corn, wheat, barley, oats, and rye) on large farms.
Just five years ago, it was impossible to use video-content analysis to operate agricultural machinery at this level of automation because there weren't any fully functional neural networks that could detect the borders of a crop strip or see any obstacles in it.
At first, we considered combining GPS with visual data analysis, but it didn't take us long to realize that visual analytics alone is enough. For a GPS steering system to work, you need to prepare a map in advance, install a base station for corrections, or purchase a package of signals. It also requires pressing a lot of buttons in a lot of menus, and combine operators have very little appreciation for user interfaces. What we offer is a camera and a box stuffed with processing power and neural networks. As soon as the camera and the box are mounted and connected to the combine's control system, we're good to go. Once in the field, the newly installed Cognitive Agro Pilot says: “Hurray, we're in the field,” asks the driver for permission to take over, and starts driving. Five years from now, we predict that all combine harvesters will be equipped with a computer vision–based autopilot capable of controlling every aspect of harvesting crops.
From a single video stream, Cognitive Agro Pilot's neural networks are able to identify crops, cleared ground, static obstacles, and moving obstacles like people or other vehicles.Cognitive Pilot
Getting to this point has meant solving some fascinating challenges. We realized we would be facing an immense diversity of field scenes that our neural network must be trained to understand. Already working with farmers on the early project stages, we found out that the same crops can look completely different in different climatic zones. Preparing for mass production of our system, we tried to compile the most highly diversified data set with various fields and crops, starting with videos filmed in the fields of several farms across Russia under different weather and lighting conditions. But it soon became evident we needed to come up with a more adaptable solution.
We decided to use a coarse-to-fine approach to train our networks for autonomous driving. The initial version is improved with each new client, as we obtain additional data on different locations and crops. We use this data to make our networks more accurate and reliable, employing unsupervised domain adaptation to recalibrate them in a short time by adding carefully randomized noise and distortions to the training images to make the networks more robust. Humans are still needed to help with semantic segmentation on new varieties of crops. Thanks to this approach, we have now obtained highly resilient all-purpose networks suitable for use on over a dozen different crops grown across Eastern Europe.
The way the Cognitive Agro Pilot drives a combine is similar to how a human driver does it. That is, our unique competitive edge is the system's ability to see and understand the situation in the field much as a human would, so it maintains full efficiency in collaboration with human drivers. At the end of the day, it all comes down to economics. One human-driven combine can harvest around 20 hectares of crops during one shift. When Cognitive Agro Pilot does the driving, the operators' workload is considerably lower: They don't get tired, can make fewer stops, and take fewer breaks. In practical terms, it means harvesting around 25 to 30 hectares per shift. For a business owner, it means that two combines equipped with our system deliver the performance of three combines without it.
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While the combine drives itself, the human operator can make adjustments to the harvesting system to maximize speed and efficiency.Cognitive Pilot
On the market now there are some separate developments from various agricultural-harvesting companies. But each of their autonomous features is done as a separate function—driving along a field edge, driving along a row, and so on. We haven't yet seen another industrial system that can drive completely with computer vision, but one-eyed Vasya showed us that this was possible. And so as we thought about cost optimization and solving the task with a minimum set of devices, we decided that for a farmer's AI-based robot assistant, one camera is enough.
The Cognitive Agro Pilot's primary sensor is a single 2-megapixel color video camera that can see a wide area in front of the vehicle, mounted on a bracket near one of the combine's side mirrors. A control unit with an Nvidia Jetson TX2 computer module is mounted inside the cab, with an integrated display and driver interface. This control unit contains the main stack of autonomy algorithms, processes the video feed, and issues commands to the combine's hydraulic systems for control of steering, acceleration, and braking. A display in the cab provides the interface for the driver and displays warnings and settings. We are not tied to any particular brand; our retrofit kit will work with any combine harvester model available in the farmer's fleet. For a combine more than five years old, interfacing with its control system may not be quite so easy (sometimes an additional steering-angle sensor is required), but the installation and calibration can still usually be done within one day, and it takes just 10 minutes to train a new driver.
Our vision-based system drives the combine, so the operator can focus on the harvest and adjusting the process to the specific features of the crop. The Cognitive Agro Pilot does all of the steering and maintains a precise distance between rows, minimizing gaps. It looks for obstacles, categorizes them, and forecasts their trajectory if they're moving. If there is time, it warns the driver to avoid the obstacles, or it decides to drive around them or slow down. It also coordinates its movement with a grain truck and with other combines when it is part of a formation. The only time that the operator is routinely required to drive is to turn the combine around at the end of a run. If you need to turn, go ahead—the Cognitive Agro Pilot releases the controls and starts looking for a new crop edge. As soon as it finds one, the robot says: “Let me do the driving, man.” You push the button, and it takes over. Everything is simple and intuitive. And since a run is normally up to 5 kilometers long, these turns account for less than 1 percent of a driver's workload.
Once in the field, the newly installed Cognitive Agro Pilot says: “Hurray, we're in the field,” asks the driver for permission to take over, and starts driving.
During our pilot project last year, the yield from the same fields increased by 3 to 5 percent due to the ability of the harvester to maintain the cut width without leaving unharvested areas. It increased an additional 3 percent simply because the operators had time to more closely monitor what was going on in front of them, optimizing the harvesting performance. With our copilot, drivers' workloads are very low. They start the system, let go of the steering wheel, and can concentrate on controlling the machinery or checking commodity prices on their phones. Harvesting weeks are a real ordeal for combine drivers, who get no rest except for some sleep at night. In one month they need to earn enough for the upcoming six, so they are exhausted. However, the drivers who were using our solution realized they even had some energy left, and those who chose to work long hours said they could easily work 2 hours more than usual.
Gaining 10 or 15 percent more working hours over the course of the harvest may sound negligible, but it means that a driver has three extra days to harvest the crops. Consequently, if there are days of bad weather (like rain that causes the grain to germinate or fall down), the probability of keeping the crop yield high is a lot greater. And since combine operators get paid by harvested volume, using our system helps them make more money. Ultimately, both drivers and managers say unanimously that harvesting has become easier, and typically the cost of the system (about US $10,000) is paid off in just one season. Combine drivers quickly get the hang of our technology—after the first few days, many drivers either start to trust in our robot as an almighty intelligence, or decide to test it to death. Some get the misconception that our robots think like humans and are a little disappointed to see that our system underperforms at night and has trouble driving in dust when multiple combines are driving in file. Even though humans can have problems in these situations also, operators would grumble: “How can it not see?” A human driver understands that the distance to the combine ahead is about 10 meters and that they are traveling at a constant speed. The dust cloud will blow away in a minute, and everything will be fine. No need to brake. Alex, the driver of the combine ahead, definitely won't brake. Or will he? Since the system hasn't spent years alongside Alex and cannot use life experience to predict his actions, it stops the combine and releases the controls. This is where human intelligence once again wins out over AI.
Turns at the end of each run are also left to human intelligence, for now. This feature never failed to amaze combine drivers but turned out to be the most challenging during tests: The immense width of the header means that a huge number of hypotheses about objects beyond the line of sight of our single camera need to be factored in. To automate this feature, we're waiting for the completion of tests on rugged terrain. We are also experimenting with our own synthetic-aperture radar technology, which can see crop edges and crop rows as radio-frequency images. This does not add much to the total solution cost, and we plan to use radar for advanced versions of our “agrodroids” intended for work in low visibility and at night.
Robot in Disguise
It takes just four parts to transform almost any human-driven combine harvester into a robot. A camera [1] mounted on a side-view mirror watches the field ahead, sending a video stream to a combined computing unit, display, and driver interface [2] in the driver's cab. A neural network analyzes the video to find crop edges and obstacles, and sends commands to the hydraulic unit [3] to control the combine. For older combines, a steering sensor [4] mounted inside a wheel provides directional feedback for precision driving. While Cognitive Pilot's system takes care of the driving, it's the job of the human operator in the cab to optimize the performance of the header [5] to harvest the crop efficiently.Cognitive Pilot
During the summer and autumn of 2020, more than 350 autonomous combines equipped with the Cognitive Agro Pilot system drove across over 160,000 hectares of fields and helped their human supervisors harvest more than 720,000 tonnes of crops from Kaliningrad on the Baltic Sea to Vladivostok in the Russian Far East. Our robots have worked more than 230,000 hours, passing 950,000 autonomous kilometers driven last year. And by the end of 2021, our system will be available in the United States and South America.
Common farmers and the end users of our solutions may have heard about driverless cars in the news or seen the words “neural network” a couple of times, but that about sums up their AI experience. So it is fascinating to hear them say things like “Look how well the segmentation has worked!” or “The neural network is doing great!” in the driver's cab.
Changing the technological paradigm takes time, so we ensure the widest possible compatibility of our solutions with existing machinery. Undoubtedly, as farmers adapt to the current innovations, we will continuously increase the autonomy of all types of machinery for all kinds of tasks.
A few years ago, I studied the work of the United Nations mission in Rwanda dealing with the issues of chronic child malnutrition. I will never forget the photographs of emaciated children. It made me think of the famine that gripped a besieged Leningrad during World War II. Some of my relatives died there and their diaries are a testament to the fact that there are few endings more horrible than death from starvation. I believe that robotic automation and AI enhancement of agricultural machinery used in high-risk farming areas or regions with a shortage of skilled workers should be the highest priority for all governments concerned with providing an adequate response to the global food-security challenges.
This article appears in the September 2021 print issue as “On Russian Farms, the Robotic Revolution Has Begun.” Continue reading
Posted in Human Robots
#439077 How Scientists Grew Human Muscles in Pig ...
The little pigs bouncing around the lab looked exceedingly normal. Yet their adorable exterior hid a remarkable secret: each piglet carried two different sets of genes. For now, both sets came from their own species. But one day, one of those sets may be human.
The piglets are chimeras—creatures with intermingled sets of genes, as if multiple entities were seamlessly mashed together. Named after the Greek lion-goat-serpent monsters, chimeras may hold the key to an endless supply of human organs and tissues for transplant. The crux is growing these human parts in another animal—one close enough in size and function to our own.
Last week, a team from the University of Minnesota unveiled two mind-bending chimeras. One was joyous little piglets, each propelled by muscles grown from a different pig. Another was pig embryos, transplanted into surrogate pigs, that developed human muscles for more than 20 days.
The study, led by Drs. Mary and Daniel Garry at the University of Minnesota, had a therapeutic point: engineering a brilliant way to replace muscle loss, especially for the muscles around our skeletons that allow us to move and navigate the world. Trauma and injury, such as from firearm wounds or car crashes, can damage muscle tissue beyond the point of repair. Unfortunately, muscles are also stubborn in that donor tissue from cadavers doesn’t usually “take” at the injury site. For now, there are no effective treatments for severe muscle death, called volumetric muscle loss.
The new human-pig hybrids are designed to tackle this problem. Muscle wasting aside, the study also points to a clever “hack” that increases the amount of human tissue inside a growing pig embryo.
If further improved, the technology could “provide an unlimited supply of organs for transplantation,” said Dr. Mary Garry to Inverse. What’s more, because the human tissue can be sourced from patients themselves, the risk of rejection by the immune system is relatively low—even when grown inside a pig.
“The shortage of organs for heart transplantation, vascular grafting, and skeletal muscle is staggering,” said Garry. Human-animal chimeras could have a “seismic impact” that transforms organ transplantation and helps solve the organ shortage crisis.
That is, if society accepts the idea of a semi-humanoid pig.
Wait…But How?
The new study took a page from previous chimera recipes.
The main ingredients and steps go like this: first, you need an embryo that lacks the ability to develop a tissue or organ. This leaves an “empty slot” of sorts that you can fill with another set of genes—pig, human, or even monkey.
Second, you need to fine-tune the recipe so that the embryos “take” the new genes, incorporating them into their bodies as if they were their own. Third, the new genes activate to instruct the growing embryo to make the necessary tissue or organs without harming the overall animal. Finally, the foreign genes need to stay put, without cells migrating to another body part—say, the brain.
Not exactly straightforward, eh? The piglets are technological wonders that mix cutting-edge gene editing with cloning technologies.
The team went for two chimeras: one with two sets of pig genes, the other with a pig and human mix. Both started with a pig embryo that can’t make its own skeletal muscles (those are the muscles surrounding your bones). Using CRISPR, the gene-editing Swiss Army Knife, they snipped out three genes that are absolutely necessary for those muscles to develop. Like hitting a bullseye with three arrows simultaneously, it’s already a technological feat.
Here’s the really clever part: the muscles around your bones have a slightly different genetic makeup than the ones that line your blood vessels or the ones that pump your heart. While the resulting pig embryos had severe muscle deformities as they developed, their hearts beat as normal. This means the gene editing cut only impacted skeletal muscles.
Then came step two: replacing the missing genes. Using a microneedle, the team injected a fertilized and slightly developed pig egg—called a blastomere—into the embryo. If left on its natural course, a blastomere eventually develops into another embryo. This step “smashes” the two sets of genes together, with the newcomer filling the muscle void. The hybrid embryo was then placed into a surrogate, and roughly four months later, chimeric piglets were born.
Equipped with foreign DNA, the little guys nevertheless seemed totally normal, nosing around the lab and running everywhere without obvious clumsy stumbles. Under the microscope, their “xenomorph” muscles were indistinguishable from run-of-the-mill average muscle tissue—no signs of damage or inflammation, and as stretchy and tough as muscles usually are. What’s more, the foreign DNA seemed to have only developed into muscles, even though they were prevalent across the body. Extensive fishing experiments found no trace of the injected set of genes inside blood vessels or the brain.
A Better Human-Pig Hybrid
Confident in their recipe, the team next repeated the experiment with human cells, with a twist. Instead of using controversial human embryonic stem cells, which are obtained from aborted fetuses, they relied on induced pluripotent stem cells (iPSCs). These are skin cells that have been reverted back into a stem cell state.
Unlike previous attempts at making human chimeras, the team then scoured the genetic landscape of how pig and human embryos develop to find any genetic “brakes” that could derail the process. One gene, TP53, stood out, which was then promptly eliminated with CRISPR.
This approach provides a way for future studies to similarly increase the efficiency of interspecies chimeras, the team said.
The human-pig embryos were then carefully grown inside surrogate pigs for less than a month, and extensively analyzed. By day 20, the hybrids had already grown detectable human skeletal muscle. Similar to the pig-pig chimeras, the team didn’t detect any signs that the human genes had sprouted cells that would eventually become neurons or other non-muscle cells.
For now, human-animal chimeras are not allowed to grow to term, in part to stem the theoretical possibility of engineering humanoid hybrid animals (shudder). However, a sentient human-pig chimera is something that the team specifically addressed. Through multiple experiments, they found no trace of human genes in the embryos’ brain stem cells 20 and 27 days into development. Similarly, human donor genes were absent in cells that would become the hybrid embryos’ reproductive cells.
Despite bioethical quandaries and legal restrictions, human-animal chimeras have taken off, both as a source of insight into human brain development and a well of personalized organs and tissues for transplant. In 2019, Japan lifted its ban on developing human brain cells inside animal embryos, as well as the term limit—to global controversy. There’s also the question of animal welfare, given that hybrid clones will essentially become involuntary organ donors.
As the debates rage on, scientists are nevertheless pushing the limits of human-animal chimeras, while treading as carefully as possible.
“Our data…support the feasibility of the generation of these interspecies chimeras, which will serve as a model for translational research or, one day, as a source for xenotransplantation,” the team said.
Image Credit: Christopher Carson on Unsplash Continue reading
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