Bill Smart, far right, Oregon State University professor of robotics, and an Amazon Scholar, demonstrates an experiment simulating how robots might be used during Ebola outbreaks.
In this 2019 photo, Bill Smart, far right, an Oregon State University professor of robotics, demonstrates how robots might be used during Ebola outbreaks. Today, he is an Amazon Scholar and is teaming up with Amazon to study how robots and people interact over prolonged periods of time.
Credit: Oregon State University

Amazon Scholar has his eyes on the future of robot movement

Learn how Bill Smart wants to simplify the ways that robots and people work together — and why waiting on a date one night changed his career path.

Mobile robots are popping up all around us. They check inventory while rolling down supermarket aisles, clean airport floors at night, supply and sanitize hospital rooms, and check petrochemical pipes for corrosion. Amazon uses more than 200,000 robots in its operations.  

People and robots are increasingly learning to live and work together. Bill Smart wants to simplify the interactions between them. Smart, a professor of robotics and associate director of the Collaborative Robotics and Intelligent Systems Institute at Oregon State University, is teaming with Amazon to study how robots and people interact over prolonged periods of time.

Working with robots at Amazon, it’s possible to think on a larger scale for months and years at a time. This lets you ask questions that you just can’t as an academic.
Bill Smart

In academia, a typical study might include 30 people. Researchers bring the participants into a room, one at a time, where they interact with a robot in a variety of ways. This may yield some insights, Smart said, but these studies take place in isolation — just one person and one robot the person has never seen before. This scenario does not really duplicate the emerging robot-people world.

“As an academic, I can run a few robots for a few days, and do what essentially amounts to proof-of-concept studies,” he said. “Working with robots at Amazon, it’s possible to think on a larger scale for months and years at a time. This lets you ask questions that you just can’t as an academic.”

And that scale, over time, is extraordinarily valuable to Smart. "I want to know what these contacts look like over a month or over a year," Smart said. "Your interaction in that first hour is going to be a lot different than your interaction at the end of a month or a year. I'm interested in how people work with robots, and also what they do when they are not working with them, but sharing the same space."

The desire to run more robust experiments led Smart to Amazon, where he is an Amazon Scholar. He plans to study how people and robots interact with one another over the long term. Smart said his familiarity with Amazon Robotics, and the people who work there, also drove his interest.

“The thing that really got me to think about joining the Scholars’ program is the people that I knew who were already at Amazon,” he said. “I knew Tye Brady [Amazon Robotics’ chief technology officer] from MARS, and I’ve known Sidd [Siddhartha Srinivasa, director of Amazon Robotics] for years. The fact that Sidd, in particular, had moved to Amazon carried a lot of weight, and gave it a lot of credibility in the AI and robotics space.”

An Amazon robot on the fulfillment center floor
One of the advantages of conducting robotics research at Amazon is scale. "You can gather statistics on things you just could not learn in a smaller setting," Bill Smart said.

Smart is not just focused on people-robot interfaces. He also works on machine learning and public policy. He has a lot on his plate for someone whose career in robotics began while waiting for a date.

From math to robots

Smart grew up in Scotland, just south of the Highlands, in "a tiny little town of 7,000 people in the middle of nowhere." His father was a factory worker, his mother did piecework, and growing up, he spent his holidays picking fruit and working in a cannery.

If he had been a little older, he likely would never have furthered his education. Instead, educational reforms at the time enabled him to become the first of his family to go to university.

Smart started in mathematics at University of Dundee, but had a realization. "Math was very abstracted from reality," he recalled. "I could push the symbols around the page, but I didn't see how it affected the world. So I switched to computer science, which used some math, but I got to apply it to problems, which was really cool."

Smart thought his career path was set. Then, one night, he went to pick up the woman he was dating. She was not ready, so he started reading a magazine article about the robots that pioneering roboticist Rodney Brooks was building at MIT. That was all it took. “I looked at one and thought, ‘Wow. That's really cool,’” he said. He was hooked.

Nearby Edinburgh University offered one of the world's first master's degrees in robotics. That eventually led to a PhD in computer science at Brown University and a thesis on machine learning in robots. He set up his own lab at Washington University in St. Louis and later took a sabbatical year at Willow Garage, a California-based robotics incubator that was developing the open source Robot Operating System (ROS) at the time. In 2012, he decamped for Oregon State University.

Working together

Smart's research involves expanding on ways for people and robots to work together over the long term. One particular area he wants to expand on is the ways in which a robot signals what it is about to do and where it intends to go next.

canvas robot.jpg
“Currently, the robots we're working with have a set of indicator lights, similar to a car, that show the intent of the robot,” Bill Smart observed. “The ultimate goal is to have the robots be ‘invisible in use’, so that the employees don’t have to think about them any more than they think about the actions of their human colleagues.”

“Currently, the robots we're working with have a set of indicator lights, similar to a car, that show the intent of the robot,” Smart observed. “The underlying safety systems on the robot will cause it to slow or stop. But the ultimate goal is to have the robots be ‘invisible in use’, so that the employees don’t have to think about them any more than they think about the actions of their human colleagues.”

One of the advantages of conducting this research at Amazon is scale. This is important for two reasons. First, it enables Smart to gather data on variations in robot behavior, like testing which side of an aisle a robot should use or how fast it should go.

"You can gather statistics on things you just could not learn in a smaller setting," he said. It also provides a more realistic framework for measuring those changes. Working at Amazon means Smart has access to the kind of scale that makes it easier to extrapolate useful results.

Large numbers also matter for machine learning. Smart's work involves turning sensor information into actionable intelligence. He views machine learning as a tool, and one that is highly effective. "That's really important in a production environment,” Smart said

Evolving robot policy

Smart, who was selected as an AAAS Leshner Leadership Institute Public Engagement Fellow in artificial intelligence, also has an interest in policy. While policy concerns stretch back over his career, his journey really began in 2011. That is when a colleague at Washington University, law professor Neil Richards, whose scholarship involves technology, suggested that Smart attend We Robot, then a new conference on the legal and policy aspects of robots and AI. Smart wound up giving the conference's first-ever presentation.

The conference was an eye-opener for Smart. It was begun by lawyers who were involved with internet law, which was written largely after the internet had already exploded into the world. The conference hoped to work through the legal implications of a world coinhabited by robots and people before those robots appeared on the scene. Smart was one of the few technologists there.

"Most of the scholarship came from the legal and policy side, and it was not strongly anchored to what the technology could actually do," he said. "They were talking about things that might become problems in decades and not things that were a problem today. This is because they misunderstood where the technology sits today."

There's still a lot of work to do, and the hard bit is where robots intersect with human activities. In five years, I think we'll still be trying to figure that out.
Bill Smart

After interacting with roboticists for years, Smart found the conference exposed him to a different set of perspectives. He wanted to contribute by helping to explain robotics without the hype. The more people understand about robots, he said, the better decisions they will make.

Smart said he is also concerned about over-reliance on the idea that robots or AI can act as a panacea, rather than providing tools to address problems. Yet he remains excited. After 20 years in the field, he is finally seeing robots in the world. Even Oregon State has begun using delivery robots, doing useful jobs that make people's lives better.

"We're still in the early days, equivalent to where computers were before Apple and IBM," he said. "There's still a lot of work to do, and the hard bit is where robots intersect with human activities. In five years, I think we'll still be trying to figure that out."

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About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. About our team The Gnome team within the Sponsored Products and Brands (SPB) improves ad selection helping shoppers reach their shopping mission. To do this, we apply a broad range of machine learning, causal inference, reinforcement learning based optimization techniques and LLMs to continuously explore, learn, and optimize ads shown. We are an interdisciplinary team with a focus on customer obsession and inventing and simplifying. Our primary focus is on improving the ads experience by gaining a deep understanding of shopper pain points and developing new innovative solutions to address them. A day in the life As an Applied Scientist on this team, you will be responsible to improve quality of ads shown using in-session and offline signals via online experimentation, ML modeling, simulation, and online feedback. As an Applied Scientist on this team, you will identify opportunities for the team to make a direct impact on customers and the search experience. You will work closely with with search and retail partner teams, software engineers and product managers to build scalable real-time ML solutions. You will have the opportunity to design, run, and analyze A/B experiments that improve the experience of millions of Amazon shoppers while driving quantifiable revenue impact while broadening your technical skillset. #GenAI
US, CA, Culver City
Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. We are seeking a highly skilled and analytical Research Scientist. You will play an integral part in the measurement and optimization of Amazon Music marketing activities. You will have the opportunity to work with a rich marketing dataset together with the marketing managers. This role will focus on developing and implementing causal models and randomized controlled trials to assess marketing effectiveness and inform strategic decision-making. This role is suitable for candidates with strong background in causal inference, statistical analysis, and data-driven problem-solving, with the ability to translate complex data into actionable insights. As a key member of our team, you will work closely with cross-functional partners to optimize marketing strategies and drive business growth. Key job responsibilities Develop Causal Models Design, build, and validate causal models to evaluate the impact of marketing campaigns and initiatives. Leverage advanced statistical methods to identify and quantify causal relationships. Conduct Randomized Controlled Trials Design and implement randomized controlled trials (RCTs) to rigorously test the effectiveness of marketing strategies. Ensure robust experimental design and proper execution to derive credible insights. Statistical Analysis and Inference Perform complex statistical analyses to interpret data from experiments and observational studies. Use statistical software and programming languages to analyze large datasets and extract meaningful patterns. Data-Driven Decision Making Collaborate with marketing teams to provide data-driven recommendations that enhance campaign performance and ROI. Present findings and insights to stakeholders in a clear and actionable manner. Collaborative Problem Solving Work closely with cross-functional teams, including marketing, product, and engineering, to identify key business questions and develop analytical solutions. Foster a culture of data-informed decision-making across the organization. Stay Current with Industry Trends Keep abreast of the latest developments in data science, causal inference, and marketing analytics. Apply new methodologies and technologies to improve the accuracy and efficiency of marketing measurement. Documentation and Reporting Maintain comprehensive documentation of models, experiments, and analytical processes. Prepare reports and presentations that effectively communicate complex analyses to non-technical audiences.