Margarita Chli, vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich, is seen standing in front of a room giving a talk.
Margarita Chli, an Amazon Research Award recipient, is vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich, where she heads up the Vision for Robotics Lab.
Lukas Bigler/wavelighthouse

How Margarita Chli is using drones to go where people can’t

When it comes to assisting search-and-rescue missions, dogs are second to none, but an Amazon Research Award recipient says they might have some competition from drones.

Today, using drones in responding to natural or man-made disasters is limited by the fact that they need to be both individually piloted and have their observations interpreted by a human. But what if drones could “see” on their own? What if they could not only make decisions about navigation, but also where to look more closely — or even collaborate with other drones and robots to observe a specific location?

That suite of skills is exactly what Margarita Chli, an Amazon Research Award recipient and vice director at the Institute of Robotics and Intelligent Systems at ETH Zurich (the Swiss Federal Institute of Technology), is exploring. Chli heads up the Vision for Robotics Lab there (V4RL), and she’s been using her 2019 Amazon Research Award (she was awarded one in 2020 as well) to advance robotic vision for small aircraft, including drones.

Chli grew up in Greece and Cyprus with math teachers as parents, so while she was “heavily trained” in the language of mathematics, she didn’t always know robotics would be her professional focus.

Chli says it was really a series of lucky events that led to her introduction to “influential and brilliant scientists who planted the seed of intellectual curiosity in this area.”

After studying computer science and engineering at the University of Cambridge, where she earned her bachelor’s and master’s degrees, she considered her options.

“The coolest thing at the time seemed to be this PhD position at Imperial College in London, where my advisor, Andrew Davison, brought me into the area of robotic vision. That’s how it all started,” says Chli.

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Davison’s expertise was pioneering monocular SLAM (simultaneous localization and mapping), which is about “understanding how a camera moves in space,” says Chli. In pursuing her PhD, Chli did a lot of coding on her laptop, connecting that computer to a single camera and testing algorithms.

During her postdoc at ETH Zurich, which began in 2010, she applied her computer-vision algorithms to small drones. Chli says it was exciting to translate what she was doing on her laptop to a robot that was actually moving. That’s when she envisioned the potential impact for this technology.

“It's one thing to write some code and look at beautiful images, and another thing to get a robot moving – you get a feeling that you're creating something. And even going beyond that, to create something that can help people,” says Chli.

Drones in disaster zones

Her time at ETH Zurich also marked an era where drones, which had once been prohibitively expensive, were becoming more popular and accessible. “The technological hardware side of things was blooming, which meant we could run then-expensive image-processing algorithms onboard smaller and smaller platforms.” Those drones were more expensive, bulkier, less flexible, and lacked the processing power compared to today’s drones, “but nevertheless, the applications and imagination were there already,” she says.

As she wrote her research proposals, Chli expanded her thinking about the power of this technology. “What can we do with this? How we can use drones and robots and robotic vision to have robots in our everyday lives, that that can help us with tasks that we don't want to do?”

Those questions have propelled her research ever since.

Margarita Chli is seen speaking behind a lectern that says ETH Zürich on it, there are two large flower vases just behind her
One of the first projects for Margarita Chli at ETH Zurich: using drones for search-and-rescue missions.
Oliver Bartenschlager

One of the first projects Chli got to work on at ETH Zurich — where she was appointed as a deputy director of the lab she was working as a postdoc — was using drones for search-and-rescue missions. That work involved drones accessing areas that would be too dangerous or time-consuming for rescuers on foot, allowing rescuers to search for missing people with less risk.

Working backwards from the end-user, Chli spoke with rescuers at Club Alpino Italiano and learned that they didn’t want anything in the field that wasn’t directly useful — drones that worked independently made more sense than dedicating human resources to flying and monitoring drones.

These rescuers had lost colleagues to this very risky work, which takes place in harsh weather conditions, and so they were understandably demanding — and skeptical. “They had no time for delays or mistakes from fussy hardware or software,” she says.

The requirement for simplicity and a just-works solution has “been a great drive for my research ever since, to be honest: to develop plug-and-play, no-fuss systems, such that mission experts do not need to also be robotics experts or pilots.”

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While supporting the work of search-and-rescue teams is still an important component of her work, Chli and team have expanded the scope of their research.

Chli also envisions drones being used for inspecting hard-to-reach areas like wind-turbine blades, or power plants. “In 2012, there was a big explosion in the power plant on the island where I come from in Cyprus. We needed drones to be able to inspect the boilers for cracks to figure out how safe it was for humans to go closer,” she says.

Truly useful robots

This incident inspired Chli to focus on designing robots with real utility.

“I found it quite astonishing that we would see in the news robots that could do all sorts of gimmicky things, but we didn’t have reliable enough robots that could really help humans in a time of dire need.” She wanted to change that, and with her background in robotic vision and interest in drones, creating an unmanned aerial vehicle (UAV) that could “see” was the next challenge. In 2013, she was part of the team that ran the first vision-based autonomous flights of a small helicopter.

Margarita Chli is seen standing on a garden terrace, a drone is hovering over her shoulder in the background.
Margarita Chli is tackling drone challenges such as how a drone can maintain estimating its motion as accurately as possible.
Daniel Winkler

That same year, Chli took a post as a professor at the University of Edinburgh as a Chancellor's fellow. There, she started Vision for Robotics Lab (V4RL), which focuses on vision for robots, especially UAVs. In 2015, she returned to ETH Zurich, where she’s now professor and continues to lead V4RL.

Her research has been accelerated thanks to the resources made available to her as an Amazon Research Award recipient; resources that include access to AWS EC2 and S3.

“I think that what Amazon is doing is a great thing, because it's helping us actually see what researchers can do with its tools and it is democratizing where research is going,” she says.

She’s using those tools to tackle some of the most important problems in her work at ETH Zurich, like “how to figure out where a good spot to land is for our drones, and how we can keep a drone estimating its motion as accurately as possible, without being affected by water, trees, pedestrians, cars, and other dynamic, moving parts of the scene.” While flight-critical tasks must be processed on the drones themselves, transferring other processing tasks to the cloud, like semantic segmentation and high-level path planning, makes sense, says Chli.

Drones helping humanity

Chli thinks drones that can see and make decisions on their own will serve humanity outside search-and-rescue operations.

Researchers tracking wildlife migrations or large, dispersed herds could use drones to keep tabs on individual animals in ways humans on foot can’t, while at the same time understanding group movements.

Robots are going to help us in many ways that today we cannot really imagine, in ways we never thought possible.
Margarita Chli

“Archaeologists have come to us and said, ‘We have about 250 archaeological sites in Greece, we have a few tools around like a tripod, and I can put it in different places and take laser scans, but it's heavy, it's bulky. I don't want to find holes in my model, because I don't have time to go back to every one of these sites to capture new data.’ That’s where drones could be ideal, because they can map an area,” says Chli.

Chli says she’s become a bit of a drone evangelist because often when people hear her speak about autonomous drones, they think of military applications — whereas her focus is on what robots can do to improve the human condition.

Chli said she understands how that distrust emerged. “This technology has been growing very quickly, particularly comparing the progress today to a few years back,” she said. “And the less we know about how this technology works, the more scared we are of it.”

That’s why, she says, it’s important to raise questions and have open dialogues to address concerns because, as she sees it, robots are going to be part of our everyday lives.

“Robots are going to help us in many ways that today we cannot really imagine,” Chli says, “in ways we never thought possible.”

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The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. We are seeking a technical leader for our Supply Science team. This team is within the Sponsored Product team, and works on complex engineering, optimization, econometric, and user-experience problems in order to deliver relevant product ads on Amazon search and detail pages world-wide. The team operates with the dual objective of enhancing the experience of Amazon shoppers and enabling the monetization of our online and mobile page properties. Our work spans ML and Data science across predictive modeling, reinforcement learning (Bandits), adaptive experimentation, causal inference, data engineering. Key job responsibilities Search Supply and Experiences, within Sponsored Products, is seeking a Senior Applied Scientist to join a fast growing team with the mandate of creating new ads experience that elevates the shopping experience for our hundreds of millions customers worldwide. We are looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model– and leveraging this knowledge to help turn the flywheel of the business. As a Senior Applied Scientist on this team you will: --Act as the technical leader in Machine Learning and drive full life-cycle Machine Learning projects. --Lead technical efforts within this team and across other teams. --Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production. --Run A/B experiments, gather data, and perform statistical analysis. --Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. --Work closely with software engineers to assist in productionizing your ML models. --Research new machine learning approaches. --Recruit Applied Scientists to the team and act as a mentor to other scientists on the team. A day in the life The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to customers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
IN, KA, Bengaluru
The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide fee strategy, quantify its impact, and ensure fees are accurately computed and explained for billions of transactions between Amazon selling partners and customers. We help build the foundations for growing selling partner businesses, bringing the best selection and prices to Amazon customers, and helping Amazon leaders make and implement high impact decisions that optimally balance profitability and growth. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact. As an data scientist on our team, this role will focus on the application of data analysis, econometrics, machine learning, and artificial intelligence to measure and predict Amazon's P&L, with emphasis on fee revenue. This blends the tools of data science, statistics, and ML/AI. Your work will shape not only how fees are decided, but how they are interpreted and planned. We are seeking scientists who are motivated by first principles, disciplined experimentation, and the technical challenge of deploying ideas at global scale. This is an opportunity to work on consequential problems where analytic rigor meets real-world complexity, and where your analysis, models, algorithms, and systems will directly influence the experience of millions of sellers. If you are driven to build elegant solutions to hard problems—and to see them operate in production at meaningful scale—we would welcome the opportunity to build with you. Key job responsibilities ** Translate ambiguous business challenges into well-defined scientific problems with measurable impact. ** Identify opportunities to improve fee revenue measurement, prediction, planning, structure, and level. ** Identify opportunities to improve measurement, and prediction of other items of the P&L, at appropriate levels of granularity. ** Design, develop, and deploy econometric or AI/ML models that improve our understanding of the relationship between fees and costs, or predict fee revenue, and other elements of the P&L. ** Partner closely with finance and fee strategy teams to formulate scientific questions, communicate results, and productionalize solutions. **Apply rigorous simulation methods to validate models and quantify business impact at scale. **Communicate scientific innovations and results clearly to cross-functional stakeholders and contribute to the broader internal and external scientific community through publications, talks, and technical artifacts. About the team Amazon’s third-party marketplace is a multibillion-dollar global service, connecting customers and sellers across through billions of transactions annually. The Seller Fee Science Team integrates economic modeling, machine learning, and artificial intelligence to guide business fee strategy, ensure fees are accurately computed for millions of products, and improve the seller experience with AI tools that support any fee related contact (understanding, audit, and dispute). We build the scientific foundation that empowers sellers to grow their businesses with clarity and confidence. Our team brings together world-class economists, physicists, mathematicians, and computer scientists to tackle diverse challenging problems that require theoretical rigor and deliver real-world impact.