Simultaneously Managed robots

How Many Robots Can a Single Individual Manage Simultaneously?
A DARPA initiative challenges previous beliefs.

Swarms of self-operating robots are being increasingly evaluated and utilized for intricate tasks, yet there remains a need for some degree of human supervision throughout these operations. This raises an important issue: How many robots—and how intricate a task—can one individual oversee before reaching their limit?
 

In research backed by the U.S. Defense Advanced Research Projects Agency (DARPA), specialists demonstrate that an individual can effectively oversee a diverse swarm of over 100 autonomous aerial and ground units, with feelings of being overwhelmed occurring only briefly during a minor part of the overall mission. For example, during a particularly demanding experiment lasting several days in an urban environment, human operators experienced an overload only 3 percent of the time. The findings were shared on November 19 in the IEEE Transactions on Field Robotics.

Julie A. Adams, who serves as the associate director for research at the Collaborative Robotics and Intelligent Systems Institute at Oregon State University, has dedicated 35 years to examining how humans interact with robots and intricate systems, including aircraft cockpits and control rooms in nuclear plants. She emphasizes that swarms of robots can assist in tasks that pose considerable risk and danger to humans, such as observing wildfires.
 

According to Adams, “Swarms are capable of continuously monitoring an area, which includes watching for fresh fires or potential looters in regions recently devastated by flames in Los Angeles.” She added, “This data can guide the deployment of limited resources, like firefighting teams or water tankers, to new fire outbreaks and hotspots or to places where fires were believed to be completely put out.”
 

Such missions may consist of various types of uncrewed ground vehicles, like the Aion Robotics R1, along with aerial drones, such as the Modal AI VOXL M500 quadcopter. During the mission, a human operator might need to allocate robots to different duties as the situation evolves. Importantly, theories developed over the years—and even in Adams's early research—indicate that a single individual can struggle to manage a large number of robots effectively.
 

Adams explains, “Historical theories and empirical findings showed that as the quantity of ground robots increased, so did the tasks for the human operator, often leading to a decline in overall effectiveness.” She points out that while earlier studies concentrated on unmanned ground vehicles (UGVs)—which have to navigate obstacles like curbs—unmanned aerial vehicles (UAVs) usually face fewer physical limitations.
 

Through DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) initiative, Adams and her team aimed to determine if these concepts held true for complex missions that involved a combination of uncrewed ground and aerial vehicles. In November 2021, at Fort Campbell in Kentucky, two human operators alternated engaging in various missions over three weeks, targeting the neutralization of an enemy target. Both operators had considerable expertise in swarm management, participating in shifts that varied from 1.5 to 3 hours daily.

Examining the Limits of Human Swarm Management
In the evaluations, the human operators were situated in a specific region at the perimeter of the testing location, utilizing a virtual simulation of the surroundings to monitor the positions of the vehicles and their assigned tasks.
The most extensive mission involved 110 drones, 30 land vehicles, and as many as 50 virtual vehicles acting as proxies for additional real vehicles. The machines were required to traverse the actual urban landscape, as well as navigate various imaginary dangers depicted by AprilTags—simple QR-like codes that symbolized fictional threats—distributed across the mission area.
DARPA heightened the difficulty of the final field test by introducing thousands of dangers and data points to aid in the search efforts. “The intricacy of the dangers was considerable,” Adams mentions, explaining that some threats necessitated simultaneous interaction from multiple robots and certain threats moved throughout the environment.
During each phase of the mission, the physiological reactions of the human operators to the tasks were tracked. For instance, sensors gathered information on heart-rate variability, body posture, and even the speed of their speech. This data was fed into a pre-existing algorithm for estimating workload levels and was instrumental in identifying when the operator's workload surpassed typical limits, which is termed an “overload state.”
Adams emphasizes that, although the exercise presented a significant degree of complexity and an extensive number of robots to oversee, the frequency and length of overload states were quite brief, lasting only a few minutes within a mission shift. “The overall percentage of predicted overload states accounted for just 3 percent of the total workload estimates from all the shifts for which we gathered data,” she states.

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The primary cause for a human leader to experience an overload is typically when they are required to develop several new strategies or evaluate which units in the firing area are ready for use. Adams observes that these discoveries imply that, in contrast to earlier beliefs, the quantity of robots might have a reduced impact on the effectiveness of human swarm management than once assumed. According to her, her team is investigating additional elements that might influence swarm operations, including various human constraints, system architectures, and UAS configurations, the outcomes of which could possibly guide the regulations for drones set by the U.S. Federal Aviation Administration.

How Amazon Is Transforming the Future of Robotics and Logistics The innovative robotic technologies developed by the company address significant logistical issues on a large scale.

Innovation typically starts with a spark of an idea—a straightforward "what if" that evolves into something significant. However, transforming that initial spark into a fully developed solution demands more than mere creativity. It necessitates resources, teamwork, and an unyielding determination to narrow the divide between concept and implementation. At Amazon, these elements converge to forge innovations that not only address present-day issues but also pave the way for the future.
 

“Innovation isn't solely about having a brilliant idea,” remarked Valerie Samzun, a key figure in Amazon’s Fulfillment Technologies and Robotics (FTR) sector. “It's about assembling the right team, utilizing the right resources, and fostering an environment that brings that idea to fruition.”
 

This mindset forms the foundation of Amazon’s robotics strategy, illustrated by Robin, an innovative robotic system aimed at managing some of the most intricate logistical challenges globally. The story of Robin, from its introduction to its implementation in fulfillment centers around the world, provides an insightful glimpse into how Amazon promotes innovation on a large scale.
 

Addressing Real-World Complexity
Amazon’s fulfillment centers manage millions of products every day, each intended for a client who expects efficiency and accuracy. The magnitude and intricacy of these operations are unmatched. Products vary greatly in size, shape, and weight, leading to an unpredictable and dynamic atmosphere where conventional robotic systems frequently struggle.
 

“Robots excel at uniformity,” Jason Messinger, a senior manager in robotics, noted. “But what do you do when every assignment is different? That’s the reality within our fulfillment centers. Robin needed to be more than accurate—it had to be flexible.”
 

Robin was engineered to pick and categorize items with speed and precision, but its functionalities go well beyond the fundamentals. The system incorporates advanced technologies in artificial intelligence, computer vision, and mechanical engineering to adapt and enhance its performance over time. This capacity for adjustment was vital for operations in fulfillment centers, where no two tasks are precisely alike.
 

“When we created Robin, our goal wasn't perfection in a controlled environment,” Messinger stated. “We aimed to adapt to the unpredictability of the real world. That’s what makes this such an exhilarating challenge.”
 

The Cooperative Nature of Innovation
The creation of Robin was a collective endeavor involving roboticists, data specialists, mechanical engineers, and operations experts. This cross-disciplinary strategy enabled the team to tackle every facet of Robin’s efficiency, from the algorithms that drive its decision-making to the resilience of its mechanical parts.

"At Amazon, collaboration is key," both Messinger and Samzun emphasized. Samzun added, "every issue is approached from different perspectives, incorporating insights from individuals who are knowledgeable in technology, operations, and user experience. This is how effective solutions are developed."
 

This teamwork also applied to testing and implementation. Robin wasn't solely assessed in a controlled setting; tests occurred in real-world environments that mirrored Amazon’s fulfillment centers. Engineers were able to observe Robin in operation, collect immediate data, and make improvements progressively.
 

"Every rollout provides us with valuable lessons," Messinger stated. "Robin didn’t just progress on paper—its evolution happened out in the field. That's the advantage of having the means and framework to conduct large-scale testing."
 

Reasons Engineers Prefer Amazon
For numerous engineers and researchers participating in the development of Robin, joining Amazon marked a major change from their past roles. In contrast to academic environments, where initiatives often stay theoretical, or smaller firms with limited resources, Amazon provides the scale, pace, and influence that few other companies can offer.

"One aspect that attracted me to Amazon was the opportunity to observe the results of my efforts," shared Megan Mitchell, who heads a group of engineers focused on manipulation hardware and systems at Amazon Robotics. "During my time in research and development, I dedicated years to investigating innovative ideas, but I rarely saw them materialize in practice. Now at Amazon, I can implement concepts in the field within months."
 

This sense of mission is a common sentiment among Amazon's engineers. The company's commitment to developing solutions that produce real results—affecting operations, customers, and the entire industry—aligns with those who desire their contributions to be significant.
 

"At Amazon, you’re not merely creating technology—you’re shaping the future," stated Mitchell. "That’s an immensely inspiring incentive. You realize that your work is not just theoretical—it’s having an impact."
 

Beyond the influence of their efforts, Amazon engineers enjoy access to outstanding resources. With cutting-edge facilities and extensive collections of real-world data, Amazon equips its team with the tools required to address even the most challenging problems.
 

"If there’s something needed to enhance the project, Amazon ensures it’s available. That changes everything," remarked Messinger.
 

The collaborative and iterative culture is another appealing factor. Engineers at Amazon are motivated to take risks, try out new ideas, and learn from setbacks. This approach not only speeds up innovation but also fosters an environment where creativity can flourish.

Robin's Influence on Operations and Safety
Since its introduction, Robin has transformed the way Amazon's fulfillment centers operate. The robot has executed billions of item selections, showcasing its dependability, flexibility, and effectiveness. Each product it processes collects important information, enabling the system to evolve continuously.
 

"Robin isn't just a machine," stated Samzun. "It's a system that learns. With each item it handles, it becomes more intelligent, quicker, and more efficient."
 

The influence of Robin goes far beyond mere productivity. By taking over boring and labor-intensive tasks, the system has enhanced safety for Amazon's employees. This focus on safety has been a major objective for Amazon, which aims to foster a secure and nurturing atmosphere for its staff.
 

"When Robin retrieves a product, it’s not solely about being quick or precise," Samzun clarified. "It’s also about improving workplace safety and streamlining operations. This benefits everyone involved."
 

A Larger Perspective on Robotics
The achievements of Robin are merely the starting point. The insights gained from its creation are influencing the trajectory of robotics at Amazon, paving the way for more sophisticated systems. These advancements will not only improve operations but also establish new benchmarks for the potential of robotics.

“This goes beyond a single robot,” remarked Mitchell. “It’s about establishing a framework for ongoing innovation. Robin has demonstrated what can be achieved, and now we are exploring ways to push the limits even further.”
 

For the team of engineers and researchers, the journey with Robin has been revolutionary. It has allowed them to engage in state-of-the-art technology, tackle intricate challenges, and create a significant impact—all while collaborating in an environment that fosters creativity and teamwork.
 

“At Amazon, you sense that you are contributing to a larger mission,” expressed Messinger. “You’re not merely addressing issues—you are developing solutions that truly count.”
 

The Future of Innovation
The narrative of Robin exemplifies the strength of determination, teamwork, and implementation. It shows that with appropriate tools and a proactive attitude, even the toughest obstacles can be surmounted. More importantly, it emphasizes the pivotal role Amazon has in influencing the future landscape of robotics and logistics.
 

“Innovation involves more than just having a grand concept,” stated Samzun. “It’s about transforming that concept into a tangible reality, something functional, and something that truly impacts lives. This is what Robin signifies, and it reflects our daily work at Amazon.”
 

Robin transcends being merely a robot—it embodies the potential that emerges when talented individuals unite to address actual challenges. As Amazon advances the limits of what robotics can accomplish, the impact of Robin will resonate in every selection, every shipment, and every move toward a more streamlined and interconnected future.

Artificial intelligence technologies have achieved remarkable successes in chess, where matches generally consist of approximately 40 moves. To address some of the most challenging mathematical questions globally, scientists have introduced a novel AI model capable of uncovering intricate solutions that may require thousands or even millions of steps. They propose that the new algorithms they've created could potentially aid in identifying rare but catastrophic occurrences like hurricanes and economic collapses when they arise.
 

Researchers are increasingly investigating the capabilities of AI to tackle mathematical challenges. For instance, Google DeepMind’s AlphaProof achieved results comparable to a silver medalist at the 2024 International Mathematical Olympiad, a competition for high school students, and OpenAI’s o3 system recently showcased a strong performance on standard problems relating to math, science, and programming.
 

In a recent study that has yet to undergo peer review, scientists from the California Institute of Technology and their collaborators addressed more difficult mathematical issues that have confounded professional mathematicians for years.
 

“When dealing with problems typical of math Olympiads, they usually consist of proofs that take 30 to 40 steps, similar in complexity to an average chess game,” explains Sergei Gukov, a theoretical physics and mathematics professor at the California Institute of Technology in Pasadena. “Our focus is on advanced research-level mathematics with solutions requiring thousands, millions, or even billions of steps.”
 

Ultimately, Gukov shares, “I am optimistic that we can address Millennium Prize problems using AI,” referencing a competition aimed at finding solutions to the world’s toughest mathematical challenges. “This may be overly ambitious, but it's beneficial to have some guiding goals. Currently, we are concentrating on problems just one level below, particularly those that have remained unresolved for many years.”

AI Addresses the Andrews-Curtis Hypothesis
In a recent investigation, Gukov and his team concentrated on the Andrews-Curtis hypothesis, which is a problem within combinatorial group theory that was introduced six decades ago. “Combinatorial group theory involves the manipulation of objects,” explains Gukov. “Consider a Rubik's cube. It represents a straightforward group with fundamental operations and manipulations—you can rotate various sections of a Rubik's cube both vertically and horizontally. The Andrews-Curtis hypothesis is akin to a supercharged Rubik's cube—instead of a 3 by 3 by 3 setups, it resembles a 100 by 100 by 100 arrangements.”
 

Although the team did not establish proof for the primary hypothesis, their innovative methodology disproved certain associated categories of problems known as potential counterexamples, which had been unresolved for roughly 25 years. These counterexamples serve as mathematical scenarios that would invalidate the hypothesis. Eliminating these counterexamples enhances the probability that the hypothesis holds true.
 

To tackle these issues, Gukov and his team employed a method that involved searching for surprising and complex solutions. “If you were to request DeepSeek, o3, ChatGPT, or similar models to resolve any of the challenges we examined, they would struggle to uncover answers,” he states. “They excel in generating expected or standard solutions, mimicking previous outcomes. These models are designed for universal use. Our focus is on lengthy sequences of steps that are difficult to discover and stand out among the statistical array of solutions.”
 

To create these “super-moves,” as termed by the researchers, Gukov and his team utilized a method known as reinforcement learning. They began by presenting the AI with simple problems, gradually increasing the difficulty as it progressed. The scientists aimed for strategies that wouldn’t demand excessive computational resources; Gukov mentions that the entire training process was executed on a single GPU.
 

Gukov points out that in reinforcement learning studies, researchers usually rely on the same 10 to 15 algorithms. “What excites me the most is that by considering ways to solve these problems with such extensive horizons, we were able to create new algorithms for AI,” he remarks.

Possible Uses Beyond Mathematical Context

According to Gukov, “these innovative algorithms may serve various purposes for individuals beyond just mathematics.” “They have the capability to identify outliers, irregularities, and rare occurrences—events that are extremely uncommon but can result in significant costs if they occur.” The uncommonness of such events has posed challenges for AI in making precise predictions—due to their atypical characteristics, there is a scarcity of historical data for models to be trained on. Being able to predict these potential and disastrous situations could aid communities in developing effective strategies for prevention and response.