Robotic semantic understanding image - 1
Technology developed by Amazon’s Robotics AI organization uses machine learning to map obstacles in warehouses and navigate more fluidly.

The quest to deploy autonomous robots within Amazon fulfillment centers

Company is testing a new class of robots that use artificial intelligence and computer vision to move freely throughout facilities.

Every day at Amazon fulfillment centers, more than half a million robots assist with stocking inventory, filling orders, and sorting packages for delivery. These robots follow directions provided by cloud-based algorithms and navigate along a grid of encoded markers. Virtual and physical barriers restrict their interactions with people, as well as where they can and cannot go.

Related content
Amazon fulfillment centers use thousands of mobile robots. To keep products moving, Amazon Robotics researchers have crafted unique solutions.

Now, the company is testing a new class of robots that use artificial intelligence and computer vision to roam freely throughout the fulfillment center (FC). They are helping associates accomplish tasks such as transporting oversized and unwieldy items through the shape-shifting maze of people, pallets, and pillars laid out across the fulfillment center floor, which can cover several dozen football fields.

“This is the first instance of AI being used in autonomous mobility at Amazon,” said Siddhartha Srinivasa, director of Amazon Robotics AI.

Experimental robot
An experimental robot being developed by Amazon’s Robotics AI organization is shown transporting containers filled with large packages through a warehouse environment.

The key to success for these new robots is what Amazon scientists call semantic understanding: the ability of robots to understand the three-dimensional structure of their world in a way that distinguishes each object in it and with knowledge about how each object behaves. With this understanding updated in real-time, the robots can safely navigate cluttered, dynamic environments.

For now, these robots are deployed in a few fulfillment centers where they are performing a narrow set of tasks. Researchers are exploring how to integrate these robots seamlessly and safely with the established processes that Amazon associates follow to fulfill millions of customer orders every day.

Related content
Teaching robots to stow items presents a challenge so large it was previously considered impossible — until now.

“We don’t develop technology for technology’s sake,” said Srinivasa. “We want to develop technology with an end goal in mind of empowering our associates to perform their activities better and safer. If we don’t integrate seamlessly end-to-end, then people will not use our technology.”

Robots today

About 10% of the items ordered from the Amazon Store are too long, wide, or otherwise unwieldy to fit in pods or on conveyor belts in many Amazon FCs. Today, FC employees transport these oversized items across the fulfillment center with pulleys and forklifts, navigating the ever-shifting maze of pods, pallets, robots, and people. The goal is to have robots handle this sometimes awkward task.

Robots in Amazon warehouse
Robots operating in Amazon warehouses must work in an always changing environment in close proximity to people, pallets, and other obstacles.

Ben Kadlec, perception lead for Amazon Robotics AI, is leading the development of the AI for the new robots. His team has deployed the robots for preliminary testing as autonomous transports for non-conveyable items.

To succeed, the robots need to be able to map their environment in real-time and understand what’s a stationary object — and what’s not — and use that information to make on-the-fly decisions about where to go, and how to avoid collisions to safely deliver the oversized items to their intended destinations.

“Navigating through those dynamic spaces is one aspect of the challenge,” he said. “The other one is working in close proximity with humans. That has to do with first recognizing that this thing in front of you is a human and it might move, you might need to keep a further distance from it to be safe, you might need to predict the direction the human is going.”

Teaching robots what’s what

We humans learn about the objects in our environment and how to safely navigate around them through curiosity and trial and error, along with the guidance of family, friends, and teachers. Kadlec and his team use machine learning.

The process begins with semantic understanding, or scene comprehension, based on data collected with the robot’s cameras and LIDAR.

“When the robot takes a picture of the world, it gets pixel values and depth measurements,” explained Lionel Gueguen, an Amazon Robotics AI machine learning applied scientist. “So, it knows at that distance, there are points in space — an obstacle of some sort. But that is the only knowledge the robot has without semantic understanding.”

Semantic understanding
The robot’s AI can differentiate between stationary and moving obstacles by layering semantics on top of sensor data so the robot behaves differently around people, pallets, or pillars in a warehouse.

Semantic understanding, he continued, is about teaching the robot to define that point in space — to determine if it belongs to a person, a pod, or a pillar. Or, if it’s a cable lying across the floor, or a forklift, or another robot.

When these labels are layered on top of the three-dimensional visual representation, the robot can then classify the point in space as stable or mobile and use that information to calculate the safest path to its destination.

“The navigation system does what we call semantically aware planning and navigation,” said Srinivasa. “The intuition is very simple: The way a robot moves around a trash can is probably going to be different from the way it navigates around a person or a precious asset. The only way the robot can know that is if it’s able to identify, ‘Oh that’s the trash can or that’s the person.’ And that’s what our AI is able to do.”

Related content
Preliminary tests show a prototype pinch-grasping robot achieved a 10-fold reduction in damage on items such as books and boxes.

To teach the robots semantics, scientists collected thousands of images taken by the robots as they navigated. Then, teams trace the shape of each object in each image and label it. Data scientists use this labeled data to train a machine learning model that segments and labels each object in the cameras’ field of view, a process known as semantic segmentation.

Layered on top of the semantic understanding are predictive models that teach the robot how to treat each object detected. When it detects a pillar, for example, it knows that pillars are static and will always be there. The team is working on another model to predict the paths of the people the robot encounters, and adjust course accordingly.

“Our work is improving the representation of static obstacles in the present as well as starting to model the near future of where the dynamic obstacles are going to be,” said Gueguen. “And that representation is passed down in such a way that the robot can plan accordingly to, on one hand, avoid static obstacles and on the other hand avoid dynamic obstacles.”

Fulfillment center deployment

Kadlec and his team have deployed a few dozen robots for preliminary testing and refinement at a few fulfillment centers. There, they are moving packages, collecting more data, and delivering insights to the science team on how to improve their real-world performance.

“It’s really exciting,” Kadlec said. “We can see the future scale that we want to be operating at. We see a clear path to being successful.”

Once Kadlec and his colleagues succeed in the full-scale deployment of autonomous mobile robot fleets that can transport precious, oversized packages, they can apply the learnings to additional robots.

“The particular problem we’re going after right now is pretty narrow, but the capability is very general,” Kadlec said.

The road ahead

Among the challenges of deploying free-roaming robots in Amazon fulfillment centers is making them acceptable to associates, Srinivasa noted.

“If the robot sneaks up on you really fast and hits the brake a millimeter before it touches you, that might be functionally safe, but not necessarily acceptable behavior,” he said. “And so, there’s an interesting question around how do you generate behavior that is not only safe and fluent, but also acceptable, that is also legible, which means that it’s human understandable.”

Related content
By managing and automating many of the steps involved in continual learning, Janus is helping Amazon’s latest robots adapt to a changing environment.

Amazon scientists who study human-robot interaction are developing techniques for robots to indicate their next move to other people without bright lights and loud sounds. One way they’re doing this is through imitation learning, where robots watch how people move around each other and learn to imitate the behavior.

The challenge of acceptance, Srinivasa said, is part of the broader challenge of seamlessly integrating robots into the process path at Amazon fulfillment centers.

“We are writing the book of robotics at Amazon,” he said, noting that it’s an ongoing process. “One of the joys of being in a place like Amazon is that we have direct access to and direct contact with our end users. We get to talk to our associates and ask them, ‘How do you feel about this?’ That internal customer feedback is critical to our development process.”

View from space of a connected network around planet Earth representing the Internet of Things.
Sign up for our newsletter

Research areas

Related content

US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Do you have a strong machine learning background and want to help build new speech and language technology? Amazon is looking for PhD students who are ready to tackle some of the most interesting research problems on the leading edge of natural language processing. We are hiring in all areas of spoken language understanding: NLP, NLU, ASR, text-to-speech (TTS), and more! 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 develop and implement novel scalable algorithms and modeling techniques to advance the state-of-the-art in technology areas at the intersection of ML, NLP, search, and deep learning. You will work side-by-side with global experts in speech and language to solve challenging groundbreaking research problems on production scale data. The ideal candidate must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. Amazon has positions available for Natural Language Processing & Speech Intern positions in multiple locations across the United States. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. Please visit our website to stay updated with the research our teams are working on: https://www.amazon.science/research-areas/conversational-ai-natural-language-processing
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. The Research team at Amazon works passionately to apply cutting-edge advances in technology to solve real-world problems. Do you have a strong machine learning background and want to help build new speech and language technology? Do you welcome the challenge to apply optimization theory into practice through experimentation and invention? Would you love to help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? At Amazon we hire research science interns to work in a number of domains including Operations Research, Optimization, Speech Technologies, Computer Vision, Robotics, and more! As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using mathematical programming techniques for complex problems, implement prototypes and work with massive datasets. Amazon has a culture of data-driven decision-making, and the expectation is that analytics are timely, accurate, innovative and actionable. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. 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. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
CA, ON, Toronto
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a PhD student interested in machine learning, natural language processing, computer vision, automated reasoning, or robotics? We are looking for skilled scientists capable of putting theory into practice through experimentation and invention, leveraging science techniques and implementing systems to work on massive datasets in an effort to tackle never-before-solved problems. 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. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for Masters or PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, 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. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. We are looking for PhD students excited about working on Automated Reasoning or Storage System problems at the intersection of theory and practice to drive innovation and provide value for our customers. AWS Automated Reasoning teams deliver tools that are called billions of times daily. Amazon development teams are integrating automated-reasoning tools such as Dafny, P, and SAW into their development processes, raising the bar on the security, durability, availability, and quality of our products. AWS Automated Reasoning teams are changing how computer systems built on top of the cloud are developed and operated. AWS Automated Reasoning teams work in areas including: Distributed proof search, SAT and SMT solvers, Reasoning about distributed systems, Automating regulatory compliance, Program analysis and synthesis, Security and privacy, Cryptography, Static analysis, Property-based testing, Model-checking, Deductive verification, compilation into mainstream programming languages, Automatic test generation, and Static and dynamic methods for concurrent systems. AWS Storage Systems teams manage trillions of objects in storage, retrieving them with predictable low latency, building software that deploys to thousands of hosts, achieving 99.999999999% (you didn’t read that wrong, that’s 11 nines!) durability. AWS storage services grapple with exciting problems at enormous scale. Amazon S3 powers businesses across the globe that make the lives of customers better every day, and forms the backbone for applications at all scales and in all industries ranging from multimedia to genomics. This scale and data diversity requires constant innovation in algorithms, systems and modeling. AWS Storage Systems teams work in areas including: Error-correcting coding and durability modeling, system and distributed system performance optimization and modeling, designing and implementing distributed, multi-tenant systems, formal verification and strong, practical assurances of correctness, bits-IOPS-Watts: the interplay between computation, performance, and energy, data compression - both general-purpose and domain specific, research challenges with storage media, both existing and emerging, and exploring the intersection between storage and quantum technologies. As an Applied Science Intern, 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. A successful candidate will be a self-starter with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment who is comfortable with ambiguity. Amazon believes that scientific innovation is essential to being the world’s most customer-centric company. Our ability to have impact at scale allows us to attract some of the brightest minds in Automated Reasoning and related fields. Our scientists work backwards to produce innovative solutions that delight our customers. Please visit https://www.amazon.science (https://www.amazon.science/) for more information.
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. 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. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, 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. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Help us develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, and more! We are combining computer vision, mobile robots, advanced end-of-arm tooling and high-degree of freedom movement to solve real-world problems at huge scale. As an intern, you will help build solutions where visual input helps the customers shop, anticipate technological advances, work with leading edge technology, focus on highly targeted customer use-cases, and launch products that solve problems for Amazon customers. 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. You will own the design and development of end-to-end systems and have the opportunity to write technical white papers, 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. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Amazon Scientist use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science
US, WA, Seattle
To ensure a great internship experience, please keep these things in mind. This is a full time internship and requires an individual to work 40 hours a week for the duration of the internship. Amazon requires an intern to be located where their assigned team is. Amazon is happy to provide relocation and housing assistance if you are located 50 miles or further from the office location. Are you a Masters or PhD student interested in machine learning? We are looking for skilled scientists capable of putting Machine Learning theory into practice through experimentation and invention, leveraging machine learning techniques (such as random forest, Bayesian networks, ensemble learning, clustering, etc.), and implementing learning systems to work on massive datasets in an effort to tackle never-before-solved problems. 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. Amazon Science gives insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists use our working backwards method to enrich the way we live and work. For more information on the Amazon Science community please visit https://www.amazon.science.