robin arm with gripper.jpg
Robin, one of the most complex stationary robot arm systems Amazon has ever built, brings many core technologies to new levels and acts as a glimpse into the possibilities of combining vision, manipulation and machine learning.
Credit: F4D Studio

Amazon’s robot arms break ground in safety and technology

While these systems look like other robot arms, they embed advanced technologies that will shape Amazon's robot fleet for years to come.

Inside an Amazon facility, employees and robots work together to ready products for customers. On one side of the building, yellow tote bins bearing partially completed orders ride down a conveyor. At the end of the conveyor, a robot arm called a palletizer/depalletizer stacks them on a pallet as if playing a three-dimensional game of Tetris.

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When an employee sees a pallet is complete, they approach the immobilized robot, slip a motorized hand truck under the pallet, and route it to shipping. From there, a truck takes it to another facility. There, a different palletizer/depalletizer places the totes on conveyors that guide them to employees who complete the order. On another side of the facility, a jumbled pile of soft mailers and boxes roll down a conveyor belt. Robin, a smaller robot arm, grabs one and rotates the parcel to scan the label. Once it knows the ZIP code, it sorts the package onto a robotic carrier for processing. If it sees any rips, tears, or illegible addresses, Robin transfers the package, via either conveyor or mobile robot, for employees to handle.

Robots are common in Amazon facilities, where more than 200,000 mobile units aid the flow of goods from inventory to shipping. Stationary robotic arms, however, are relatively new. Yet they play an important role in the company's drive to safely deliver the right goods to the right customers at the right time.

“A real gain for the overall system”

Although Robin and the palletizer/depalletizer look like other robot arms, they embed advanced technologies that will shape Amazon's robot fleet for years to come.

Conventional robots often do a single job — welding a section of a vehicle frame or screwing a part into place — whereas for robotic arms like Robin, few tasks are ever precisely the same.

Robin, for example, must calculate how to identify, move, and sort parcels that may rest atop one another as they are presented via a conveyor. The palletizer/depalletizer must calculate how to stack a stable pallet on the fly. To do it, they use both cutting-edge AI algorithms that make decisions in fractions of a second and high-tech cameras, sensors, and grippers.

Watch Robin deftly handle packages

While the robotic arms aid in the operation of Amazon facilities, they also improve the employee experience by eliminating repetitive lifting, stacking, and turning. In turn, this allows employees to focus on the kinds of assignments that leave robots struggling.

"Eliminating tasks that are repetitious and dull lets employees focus on things that are really important," Tye Brady, chief technologist for Amazon Robotics, observed. "If we can elevate our employees to do higher-level tasks that require common sense — something computers are not good at — that's a real gain for the overall system."

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This intricate collaboration of people and machines has helped Amazon to deliver goods with fewer mistakes, Brady said. It has also fueled growth and jobs. Since 2012, when Amazon first began deploying robots within its fulfillment centers, the company’s facility workforce added hundreds of thousands of new employees, even before its massive COVID-19 hiring efforts in 2020.

"Amazon could not have achieved what it has done without robotics, nor could we have done it without the amazing skills of our employees," Brady said. "They go hand in hand. If you try to separate one from another, you are going down a failed path."

@F4DStudio_AmazonScience_RoboticArm-00947 (1).jpg
Robin must calculate how to identify, move, and sort parcels that may rest atop one another as they are presented via a conveyor.
Credit: F4D Studio

But before Amazon could blaze that path, it first had to make sure its new robots were safe.

Safety first (and always)

"We don't just build a robot and then say, 'Hey, safety people, I want you to get involved now,'" Brady said. "Instead, safety engineers are there every step of the way, from design and deployment to maintenance and operation. They're at the table talking with us about how we can make it a better experience for our employees."

Clay Flannigan, senior manager, advanced robotics, and the technical lead in the Robin program noted that when robot and safety team members assess the flow of work in Amazon facilities, they insist on solutions that will not compromise safety.

"We work hard to identify any potential hazards," Flannigan said. "That could be anything from limiting any risk for contact between people and the robot, tripping on a floor cable, or a sharp edge on a barrier. Ideally, we can eliminate them with multiple engineering mitigations.”

This is especially important when working with large industrial robots: the best approach is to ensure appropriate access controls are implemented. This starts with fences. To enter the robot area, employees gain access through a secured gate, which positively disables the robot. There is only one gate, which provides strict control over who can access the robot.

Watch a hardware engineer operate Robin

In addition to the gate, a light curtain protects the opposite side of the robot. If an employee breaks the plane of the curtain, the robot automatically stops. These safety features ensure that the robot can do its job while permitting safe access to the area for employees to perform maintenance.

Amazon also brings in independent experts to assess the industrial designs. "They ask a lot of good questions,” Brady said. "'Can I approach the station from a weird angle? Could I open the door without the sensors tripping? Can I break the light curtain somehow without the system noticing?'"

Engineers then build and test physical prototypes, monitoring them to see if workers could potentially interact with them in ways that might cause usability issues. They also track metrics about how the machines behave within facilities, which permits continued improvement of their performance.

Robin as an evolutionary step

Robin, one of the most complex stationary robot arm systems Amazon has ever built, brings many core technologies to new levels and acts as a glimpse into the possibilities of combining vision, package manipulation and machine learning, said Will Harris, principal product manager of the Robin program.

Those technologies can be seen when Robin goes to work. As soft mailers and boxes move down the conveyor line, Robin must break the jumble down into individual items. This is called image segmentation. People do it automatically, but for a long time, robots only saw a solid blob of pixels.

Robin robotic arms sort and move packages
Robots are common in Amazon facilities, where more than 200,000 mobile units aid the flow of goods from inventory to shipping. Stationary robotic arms, however, are relatively new. Yet they play an important role in company's drive to safely deliver the right goods to the right customers at the right time.
Credit: F4D Studio

Over many years, AI algorithms have learned to break up that blob into individual objects by recognizing things like color or significant features, such as the edge of a mailer. More recently, neural networks have improved enough to do this well. The neural networks are aided in this task by training to segment mailers in a virtual world.

Engineers start by creating a virtual model of the arm and an ever-changing jumble of packages moving down a virtual conveyor belt. In the model, the robot’s AI attempts to segment and grab the items, iterating on each success and failure, and slowly learning to recognize mailers, even when they are obscured or in odd positions. After each session, the model reshuffles the packages randomly and the training begins again.  

After thousands of virtual model-training iterations, Amazon tests a prototype at its facilities. "We put together a 1,000-package test set that mirrors the profile of mail we expect to see in the building and run it through multiple times," Harris said. "This gives us good predictive data about how it will perform in the field. Then we test it operationally at select sites, before rolling it out to the entire installed base."

Pallet Tetris

The palletizer/depalletizer is a larger and more powerful machine than Robin, and a marvel of technology in its own right. It also plays a critical role in Amazon’s fulfillment operations.

See a palletizer/de-palletizer in action — skip ahead to the 24 second mark

At Amazon, Brady explains, product always flows toward employees. When someone places an order, a mobile robot brings the goods to an employee. If the order is complete, it is sent to the conveyer to be packed out and shipped to customers’ doors. If not — because no single facility contains the millions of products Amazon sells — the order goes to another facility to be completed.

The best way to ship totes is to put them on a pallet. The palletizer/depalletizer’s job is to stack totes on the pallet when they leave the facility and take them off the pallet and place them on conveyors when they come in. Brady likens the process to playing Tetris.

The palletizing starts with yellow totes parked at the end of a conveyor. All are the same size and oriented in the same direction, and they have been scanned to make sure the correct products are in the tote and the tote itself is in good shape.

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The palletizer/depalletizer has a two-dimensional camera at its tip, which it uses to rapidly position its arm over the tote. At the end is a custom gripper with four moveable L-shaped elements on each side that slip under the tote's raised upper perimeter. Once it has secured the tote, the robot lifts, pivots, and places the tote on a pallet, using a three-dimensional camera. As it does this, the AI system calculates where to place the tote so that the pallet is evenly balanced and stable. The robot builds six pallets at a time, three on each side, and it moves quickly.

"As the robot builds the pallets, people monitor several robots to make sure everything is going well," Brady said. "When the pallets are complete, they move them out of the cage with a motorized hand truck. You have this rhythm, this dynamic, between our employees and the palletizer/depalletizer, and this keeps all our operations running smoothly."

A dynamic partnership

The increasing reliance on systems like Robin and the palletizer/depalletizer also serves to highlight the symbiotic nature of the partnership between people and robots.

"There's a misconception about the sort of things we can achieve with robotic systems of this type," Flannigan answered. "There's a whole lot of tasks that we just can't solve today with robots alone and they tend to be ones that require higher levels of cognition or dexterity."

In fact, Brady noted, Amazon's facilities work best when people and machines work together: “There's a lot of productivity that involves people and machines working together, and I'm not just talking about one machine. I'm talking about an array of machines in our facilities and how we design those machines to interface with people. We use those machines to help people identify inventory, move inventory, store inventory, and source inventory. That's crucial to our job. Our employees are the backbone of our fulfillment process and we want to empower them with better machines.”

The result, says Brady, is an intricate dance, with people and machines each doing what they do best. It is one of the key reasons why Amazon continues to operate so smoothly and add tens of thousands of new jobs every year. And it’s why Amazon can deliver the right goods to the right customers at the right time.

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The Worldwide Defect Elimination (WWDE) Team is seeking a highly skilled economist to estimate the customer impact of each Customer Service action. Your analysis will assist teams across Amazon to prioritize defect elimination efforts and optimize how we respond to customer contacts. You will partner closely with our product, program, and engineering teams to deliver your findings to users via systems and dashboards that guide Customer Service planning and policies. Key job responsibilities - Develop causal, economic, and machine learning models at scale. - Engage in economic analysis; raise the bar for research. - Inform strategic discussions with senior leaders across the company to guide policies. A day in the life We thrive on solving challenging problems to innovate for our customers. By pushing the boundaries of technology, we create unparalleled experiences that enable us to rapidly adapt in a dynamic environment. Our decisions are guided by data, and we collaborate with engineering, science, and product teams to foster an innovative learning environment. If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: * Medical, Dental, and Vision Coverage * Maternity and Parental Leave Options * Paid Time Off (PTO) * 401(k) Plan About the team The WWDE team's mission is to understand and resolve all issues impacting customers and connect all organizations in Amazon to customer experiences. Our vision is to be the ultimate steward of the Voice of the Customer (VoC), empowering CS and Amazon teams to easily measure, listen, and act on customer feedback. The team broadly supports defect detection, root cause identification, and resolution to earn customer trust. The Customer Service Economics & Optimization team is a force multiplier within this group. Through causal analysis, we estimate the effectiveness of our efforts to delight the customer
US, WA, Seattle
We are seeking an experienced and innovative Battery Research Scientist III to lead advanced research initiatives in battery cell technology and safety. This senior-level position will focus on pushing the boundaries of current battery technology while developing cutting-edge solutions for thermal runaway mitigation and protection. Key Responsibilities: 1. Battery Cell Technology Research: -Lead the development of next-generation battery cell architectures, focusing on improving energy density, power output, and cycle life. -Spearhead research into novel electrode materials, electrolytes, and separators. -Design and oversee complex experiments to evaluate battery performance, degradation mechanisms, and failure modes. -Develop predictive models for battery performance and lifespan. 2. Thermal Runaway Mitigation and Protection Research: -Direct research efforts in advanced thermal management solutions for high-capacity lithium-ion batteries. -Pioneer innovative materials and designs to enhance battery safety and prevent thermal runaway events. -Oversee the development and implementation of sophisticated thermal runaway tests and failure mode analyses. -Lead the creation of industry-leading safety protocols for battery testing, manufacturing, and usage. 3. Leadership and Project Management: -Serve as principal investigator on major research projects, managing budgets, timelines, and deliverables. -Mentor junior scientists and provide technical leadership to cross-functional teams. -Collaborate with senior management to align research goals with organizational objectives. -Establish and maintain partnerships with academic institutions and industry leaders. 4. Innovation and Intellectual Property: -Drive the creation of new intellectual property, leading patent application processes. -Represent the organization at high-level conferences and industry events. -Author and co-author publications in top-tier scientific journals. -Identify emerging trends in battery technology and propose strategic research directions. Amazon.com is an Equal Opportunity-Affirmative Action Employer - Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.#0000
US, WA, Bellevue
At AWS, we use Artificial Intelligence to be able to identify every need of a customer across all AWS services before they have to tell us about it and help customers adopt best practices while architecting on the cloud. We are looking for Applied Scientists to drive innovation with Gen AI to bring paradigm shift to how the business operates and build “best in the world” experience that customers will love! Some of the science challenges we work on include fine-tuning Large language models for domain specific use cases, Reinforcement Learning, Auto-generating code from natural language and generating strategic insights and recommendations from very large datasets. You will have an opportunity to lead, invent, and design tech that will directly impact every customer across all AWS services. We are building industry-leading technology that cuts across a wide range of ML techniques from Natural Language Processing to Deep Learning and Generative Artificial Intelligence. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system. Our team packs a punch with principal level product, science, engineering, and leadership talent. We are a results focused team and you have the opportunity to lead and establish a culture for the big things to come. We combine the culture of a startup, the innovation and creativity of a R&D Lab, the work-life balance of a mature organization, and technical challenges at the scale of AWS. We offer a playground of opportunities for builders to build, have fun, and make history! AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Key job responsibilities - Deliver real world production systems at AWS scale. - Work closely with the business to understand the problem space, identify the opportunities and formulate the problems. - Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems. - Analyze and extract relevant information from large amounts of data and derive useful insights. - Work with software engineering teams to deliver production systems with your ML models - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, CA, Sunnyvale
The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Applied Scientist with strong machine learning background to build state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality - Research in advanced customer understanding and behavior modeling techniques - Collaborate with cross-functional teams of scientists, engineers, and product managers to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification - Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results - Think Big on conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports About the team The AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.