Amazon Scout making a delivery in a residential neighborhood.
Amazon Scout delivery robots are slowly shuttling around four areas in the United States: Snohomish County, Wash.; Irvine, Calif.; Franklin, Tenn.; and Atlanta, Georgia. Amazon scientists are working to help the fully autonomous delivery robots traverse a nearly infinite range of variables.

How Amazon scientists are helping the Scout delivery device find a path to success

Navigation, perception, simulation — three key components to giving Amazon Scout true independence.

Introduced in January 2019, Amazon’s Scout delivery robot now is slowly shuttling around four areas in the United States: Snohomish County, Wash.; Irvine, Calif.; Franklin, Tenn.; and Atlanta, Georgia. The electrically powered, cooler-sized delivery system is designed to find its way along sidewalks and navigate around pets, people, and a wide variety of other things it encounters while delivering packages to customers’ homes.

To deploy a fleet of fully autonomous delivery robots, Scout must manage changing weather conditions, variations in terrain, unexpected obstacles — a nearly infinite range of variables.

To better understand how Amazon Scout is working to meet those challenges, Amazon Science recently spoke with three scientists who are currently — or were formerly — professors in the robotics field, and now are working on critical components of the service. They are focusing on giving Amazon Scout the tools it needs to navigate to customers by helping the delivery robot see and understand what’s going on around it and giving it an accurate picture of the physical world.

Navigation: Where should Scout go?

Paul Reverdy, an applied scientist, is a relative newcomer to the Scout project, joining Amazon in July 2020. His background in helping automated systems such as robots work with people is extensive, including earning his PhD from Princeton University, his postdoctoral fellowship at the University of Pennsylvania, and his tenure as an assistant professor in aerospace and mechanical engineering at the University of Arizona.

Paul Reverdy
Paul Reverdy
Lamont W. Abrams Jr.

As a key contributor to Scout’s ability to find its way around a neighborhood, Reverdy has a big task. Traditional methods, such as relying on GPS signals, are not adequate to guide Scout, he says. They simply don’t offer enough detail nor are they available all the time.

“Scout has to make a lot of decisions,” Reverdy said. “Some are pretty high level, such as deciding whether it should cross a street or not. Then there are very discrete decisions it must make, such as ‘Can I get through the gap between the hedge and the trash can?’”

That’s where navigation plays a role. Rather than sending a device into territory it doesn’t fully comprehend, Reverdy is creating detailed maps of the world Scout travels within to make sure Scout has the information it needs to plan and react to the world.

“There might be bumps on a sidewalk, or it might be raining, and the sidewalk looks different,” says Reverdy. “Or it could be a higher-level decision: ‘OK, the sidewalk is blocked. Do I try to maneuver into the street? Do I try to navigate around the obstacle?’”

Scout also needs to figure these things out with a modest sensor array. “We have real-world constraints,” says Reverdy. “We need to be intelligent with our sensor data to make sure we perform.”

For Reverdy, the work with Amazon has been an interesting contrast to academia. “The thing that’s really different is working on large-scale software problems,” he says. “In academia you’re often working on your own. At Amazon, things are much more collaborative. Plus, the scale of problems we can look at is substantially larger.”

Perception: Giving Scout a view of the world

Another scientist playing a key role in giving Scout true independence is Hamed Pirsiavash, an Amazon visiting scientist, an assistant professor at the University of Maryland Baltimore County who works on computer vision and machine learning. His job is to help Scout see the world around it and understand what it is seeing or sensing.

Hamed Pirsiavash
Hamed Pirsiavash

“Scout needs to understand what a drivable area is, or what it means when it comes to a stoplight,” says Pirsiavash. “The goal is similar to self-driving cars, with the main difference that Scout mostly travels slowly on sidewalks.”

In some ways, that makes it easier for Scout to understand its environment. In other ways, the task of traversing neighborhood sidewalks is more difficult. Roads are somewhat more predictable — after all, they’re designed for cars. But sidewalks have more varied uses. “It’s a different environment from a street” says Pirsiavash, “as we’re likely to encounter a variety of obstacles, from lawn and garden tools and skateboard ramps, to outdoor furniture and toys.”

What makes Scout possible today are the big advances in computer vision and machine learning that have occurred in the past decade. “The field is advancing every day,” says Pirsiavash. “With large-scale data sets and vast computation now available, we’re able to build a robot that understands the world in a much more sophisticated way.”

For Pirsiavash, Amazon offers a chance to work on real-world, applied-science problems together with more theoretical academic challenges.  “Scout has to manage some challenging situations,” Pirsiavash says. “We’ve had cases where a Scout has encountered a basketball hoop that fell across the sidewalk. And of course, people always put their trash bins in different places, and Scout must understand what is happening.”

“I’m really enjoying the work. It’s great to see the results of our work in the field and see how it can benefit people.”

Simulation: Building a virtual world for Scout

Airlines train pilots in simulators so they can learn in a digital jetliner before taking the helm of a real aircraft. Giving Scout the tools it needs to succeed is no different: Detailed simulators give Scout the chance to test its skills in a digital environment.

Benjamin Kunsberg calls it a “digital sandbox” for the robot. “We can give Scout a world with tremendous detail, down to individual blades of grass,” he says.

Benjamin Kunsberg
Benjamin Kunsberg

Kunsberg is an Amazon applied scientist who joined the Scout team in 2019, following four years as an assistant professor of applied mathematics at Brown University in Rhode Island. Previously, he earned his PhD in applied mathematics from Yale University, and a master’s degree in mathematics from Stanford University.

Creating a digital world is a challenging task. It must be accurate enough for Scout to really get a sense of the world, and even small shifts in daylight can have an impact on that. “Small differences not taken into account can make a big difference,” says Kunsberg. “There’s dust in the air, or sun glare.”

In a way, it’s a problem from the movie, “The Matrix”. There, computers designed a virtual world. But how did they know if they got it right? “For some objects, you have no idea how accurate your digital simulation is,” says Kunsberg. “You have to work very hard to come up with benchmarks.”

In some cases, the simulation includes digital scenery similar to a video game. Engineers can add October leaves to a sidewalk, for instance, so Scout can learn that things have changed compared to April. In other cases, the Scout team uses actual photography for training, with team members then outlining and identifying key features to guide the robot’s decisions. That’s slow, but accurate, and can be combined with fully digital simulation to create an accurate view of the world.

Amazon Scout could one day be traversing your neighborhood.

Once that world is designed, Scout needs to be trained to understand it. That’s accomplished in part using neural networks — computer systems that recognize relationships among data through a process that, in part, mimics the human brain an approach not available 10 years ago.

Kunsberg has enjoyed the jump from academia to industry.

“This project involves a lot of ideas I had already been thinking about.

“I’ve been really impressed by the graphical engineers and software developers on our team. There’s really no equal in academia.”

What’s next for Scout?

It’s still Day One for Amazon Scout. The team is excited about the positive feedback from customers and results from field tests. The team expects to apply its learnings to keep moving forward on this new delivery system and on Amazon’s path to net zero carbon by 2040.

You can find out more about the team and available jobs here.

Research areas

Related content

US, WA, Seattle
Amazon is seeking an experienced, self-directed data scientist to support the research and analytical needs of Amazon Web Services' Sales teams. This is a unique opportunity to invent new ways of leveraging our large, complex data streams to automate sales efforts and to accelerate our customers' journey to the cloud. This is a high-visibility role with significant impact potential. You, as the right candidate, are adept at executing every stage of the machine learning development life cycle in a business setting; from initial requirements gathering to through final model deployment, including adoption measurement and improvement. You will be working with large volumes of structured and unstructured data spread across multiple databases and can design and implement data pipelines to clean and merge these data for research and modeling. Beyond mathematical understanding, you have a deep intuition for machine learning algorithms that allows you to translate business problems into the right machine learning, data science, and/or statistical solutions. You’re able to pick up and grasp new research and identify applications or extensions within the team. You’re talented at communicating your results clearly to business owners in concise, non-technical language. Key job responsibilities • Work with a team of analytics & insights leads, data scientists and engineers to define business problems. • Research, develop, and deliver machine learning & statistical solutions in close partnership with end users, other science and engineering teams, and business stakeholders. • Use AWS services like SageMaker to deploy scalable ML models in the cloud. • Examples of projects include modeling usage of AWS services to optimize sales planning, recommending sales plays based on historical patterns, and building a sales-facing alert system using anomaly detection.
US, WA, Seattle
We are a team of doers working passionately to apply cutting-edge advances in deep learning in the life sciences to solve real-world problems. As a Senior Applied Science Manager you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the leading edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. Location is in Seattle, US Embrace Diversity Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust Balance Work and Life Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives Mentor & Grow Careers Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities • Manage high performing engineering and science teams • Hire and develop top-performing engineers, scientists, and other managers • Develop and execute on project plans and delivery commitments • Work with business, data science, software engineer, biological, and product leaders to help define product requirements and with managers, scientists, and engineers to execute on them • Build and maintain world-class customer experience and operational excellence for your deliverables
US, Virtual
The Amazon Economics Team is hiring Interns in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.