How a universal model is helping one generation of Amazon robots train the next

New approach can cut the setup time required to develop vision-based machine learning solutions from between six to twelve months to one or two.

A fundamental theme at Amazon is movement. Obtaining a product ordered by a customer and moving that product as quickly and efficiently as possible from its source to the customer’s doorstep.

This video shows robots moving packages around an Amazon fulfillment center.

That journey will often take a package through multiple warehouses and include loadings, unloadings, sortings, and routings. Human associates are crucial to this process and so, increasingly, are robotic manipulators. A rising star in this department is the Robin robotic arm and the computer vision system that makes it possible.

Robin’s visual-perception algorithms can identify and locate packages on a conveyor belt below it, for example, and even distinguish individual packages and their type within a cluttered pile.

This perceptive ability is known as segmentation, and it is central to the development of flexible and adaptive robotic processes for Amazon fulfillment centers. That’s because packages vary enormously in their dimensions and physical characteristics, moving amid an ever-changing mix of packages and against varying backdrops.

Amazon's Robin robot arm is seen lifting packages
Robin’s visual-perception algorithms can identify and locate packages on a conveyor belt below it, for example, and even distinguish individual packages and their type within a cluttered pile.

Robin is a maturing technology, but there is a constant simmering of new ideas just below the surface at Amazon, with teams of scientists and engineers across the Amazon Robotics AI group and beyond collaborating to develop AI-powered robotic solutions to improve warehouse efficiency. A new modeling approach aims to serve them all.

An abundance of packages — but not data

The initial challenge for these early-stage collaborations is often the same.

“The biggest problem that new project teams usually face is data scarcity,” says Cassie Meeker, an Amazon Robotics AI applied scientist, based in Seattle. Obtaining images relevant to a warehouse process of interest takes time and resources, but that’s just the beginning.

Cassie Meeker, an Amazon Robotics AI applied scientist, is seen standing in front of a Robin robot arm
Cassie Meeker, an Amazon Robotics AI applied scientist, says she and her team started their quest to develop universal models by utilizing publicly available datasets to give their model basic classification skills.

“For some machine learning models, you must annotate each training image manually by drawing multiple polygons around the various packages in the picture,” Meeker explains. “It can take five minutes to annotate just one image if it’s cluttered.”

The lack of task-specific training data means teams might base their perceptual models on just a few hundred images, says Meeker: “If they're lucky, they have a thousand. But even a thousand images aren’t a lot for training a model.”

If new projects do not have sufficient variety in their training data, that’s a challenge.

“The production environment is typically very different to a prototyping environment, so when they go into the production phase on the warehouse floor, they will suddenly see all these things they've never seen before and that their perception system can’t identify,” says Meeker. “They could be setting themselves up for failure.”

This difficulty in obtaining data to train segmentation models is partly due to the very specific subject matter: packages. Many computer vision models are trained on enormous, publicly available datasets full of annotated imagery, including everything from aardvarks to zabaglione. A social media company might want to segment faces, or dogs or cats, because that’s what people have lots of pictures of.

“Many publicly available datasets are perfect for that,” says Meeker. “But at Amazon, we have such a specific application and annotation requirements. It just doesn’t translate well from cat pics.”

A ’universal model’ for packages

In short, building a dataset big enough to train a demanding machine learning model requires time and resources, with no guarantee that the novel robotic process you are working toward will prove successful. This became a recurring issue for Amazon Robotics AI. So this year, work began in earnest to address the data scarcity problem. The solution: a “universal model” able to generalize to virtually any package segmentation task.

To develop the model, Meeker and her colleagues first used publicly available datasets to give their model basic classification skills — being able to distinguish boxes or packages from other things, for example. Next, they honed the model, teaching it to distinguish between many types of packaging in warehouse settings — from plastic bags to padded mailers to cardboard boxes of varying appearance — using a trove of training data compiled by the Robin program and half a dozen other Amazon teams over the last few years. This dataset comprised almost half a million annotated images.

Meet the Amazon robot improving safety

Crucially, these images of packages were snapped from a variety of angles — not only straight down from above a conveyor belt — and against a variety of backgrounds. The sheer number and variation of images make the dataset useful in virtually any warehouse location that may benefit from robotic perception and manipulation.

Meeker estimates that starting a project with the universal model can slash the setup time required to develop vision-based ML solutions from between six to twelve months to just one or two. And it has been made available to other Amazon teams in a user-friendly form, so extensive machine learning expertise is not required.

The universal model has already demonstrated its prowess, courtesy of a project run by Amazon Robotics, called Cardinal. Cardinal is a prototype robotic arm-based system that perceives and picks up packages and places them neatly into large containers ready for transport on delivery trucks. Cardinal’s perception system was developed before the universal model was available, so the team spent a lot of time creating a bespoke training dataset for it, says Cardinal’s perception lead, Jeroen van Baar, an Amazon Robotics senior applied scientist, based in North Reading, Massachusetts.

This video shows Cardinal training itself to distinguish between package types.

“We trained the system using 25,000 annotated training images that we created ourselves. But those early training images were taken using a setup with a different appearance to our prototype Cardinal workstation,” van Baar says. “To achieve the performance that we initially desired, we had to fine-tune our model using a thousand new training images taken from that prototype setting.”

After being updated with only those new images, the universal model was as accurate for performing Cardinal’s task as the workstation’s own robust model.

“Had it been available sooner, I would only have captured data specific to our setup and fine-tuned the universal model from there,” says van Baar. “Being able to shorten training time so significantly is a major benefit.”

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

And that’s the point. The universal model can quickly capitalize on any training data produced by a new-project team. This means that when new ideas are tested on the warehouse floor, or existing methods are transplanted to a new Amazon region where things are done slightly differently, the model will have enough data diversity to handle the differences.

Siddhartha Srinivasa, director of Robotics AI, thinks of the universal model as a supportive scaffold that you can use to build your house.

“We're not advocating that everybody live in the same house,” he says. “We're advocating that Amazon teams leverage the scaffolding we're providing to build whatever house they want, because it’s already very powerful, and it is getting better every day.”

Tipping point

Only recently has all this become possible.

“The Robotics AI program is young,” says Meeker. “In the beginning, there was no reason to use other teams’ data, because no one had very much.” But a tipping point has arrived. “We now have enough mature teams in production that we are seeing a real diversity and scaling of data. It is finally generalizable.”

Indeed, while the immediate focus of universal models is identifying and localizing various package types, diverse image data is now accumulating across a range of Amazon programs that cover more aspects of fulfillment centers.

Related content
Why detecting damage is so tricky at Amazon’s scale — and how researchers are training robots to help with that gargantuan task.

The universal model now includes images of unpackaged items, too, allowing it to perform segmentation across a greater diversity of warehouse processes. Initiatives such as multimodal identification, which aims to visually identify items without needing to see a barcode, and the automated damage detection program are accruing product-specific data that could be fed into the universal model, as well as images taken on the fulfillment center floor by the autonomous robots that carry crates of products.

“We’re moving towards a situation in which even data collected by small projects run by interns can be fed into the universal base model, incrementally improving the productivity of the entire robot fleet,” says Srinivasa.

We’re moving towards a situation in which even data collected by small projects run by interns can be fed into the universal base model, incrementally improving the productivity of the entire robot fleet.
Siddhartha Srinivasa

This diversity of data and its aggregation is particularly important for robotic perception within Amazon, especially given customers’ shifting needs, frequently novel Amazon packaging, and the company’s commitment to sustainability that means shipping more items in their own unique packaging.

All of this increases the visual variety of products and packages, making it harder for robots to identify from an image where one package ends and another begins.

Feeding the universal model in this way and having it available to new teams will accelerate the experimentation and deployment of future robotic processes. The use of the universal model is factored into Amazon’s immediate operational plans.

“We’re not doing this because it's cool — though it really is cool — but because it is inevitable,” says Srinivasa.

Related content

US, WA, Seattle
Are you excited about building high-performance robotic systems that can perceive, learn, and act intelligently alongside humans? The Robotics AI team is creating new science products and technologies that make this possible, at Amazon scale. We work at the intersection of computer vision, machine learning, robotic manipulation, navigation, and human-robot interaction.The Amazon Robotics team is seeking broad, curious applied scientists and engineering interns to join our diverse, full-stack team. In addition to designing, building, and delivering end-to-end robotic systems, our team is responsible for core infrastructure and tools that serve as the backbone of our robotic applications, enabling roboticists, applied scientists, software and hardware engineers to collaborate and deploy systems in the lab and in the field. Come join us!
US, VA, Arlington
The Central Science Team within Amazon’s People Experience and Technology org (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, well-being, and the value of work to Amazonians. We are an interdisciplinary team, which combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. As Director for PXT Central Science Technology, you will be responsible for leading multiple teams through rapidly evolving complex demands and define, develop, deliver and execute on our science roadmap and vision. You will provide thought leadership to scientists and engineers to invent and implement scalable machine learning recommendations and data driven algorithms supporting flexible UI frameworks. You will manage and be responsible for delivering some of our most strategic technical initiatives. You will design, develop and operate new, highly scalable software systems that support Amazon’s efforts to be Earth’s Best Employer and have a significant impact on Amazon’s commitment to our employees and communities where we both serve and employ 1.3 million Amazonians. As Director of Applied Science, you will be part of the larger technical leadership community at Amazon. This community forms the backbone of the company, plays a critical role in the broad business planning, works closely with senior executives to develop business targets and resource requirements, influences our long-term technical and business strategy, helps hire and develop engineering leaders and developers, and ultimately enables us to deliver engineering innovations.This role is posted for Arlington, VA, but we are flexible on location at many of our offices in the US and Canada.
US, VA, Arlington
Employer: Services LLCPosition: Data Scientist IILocation: Arlington, VAMultiple Positions Available1. Manage and execute entire projects or components of large projects from start to finish including data gathering and manipulation, synthesis and modeling, problem solving, and communication of insights and recommendations.2. Oversee the development and implementation of data integration and analytic strategies to support population health initiatives.3. Leverage big data to explore and introduce areas of analytics and technologies.4. Analyze data to identify opportunities to impact populations.5. Perform advanced integrated comprehensive reporting, consultative, and analytical expertise to provide healthcare cost and utilization data and translate findings into actionable information for internal and external stakeholders.6. Oversee the collection of data, ensuring timelines are met, data is accurate and within established format.7. Act as a data and technical resource and escalation point for data issues, ensuring they are brought to resolution.8. Serve as the subject matter expert on health care benefits data modeling, system architecture, data governance, and business intelligence tools. #0000
US, TX, Dallas
Employer: Services LLCPosition: Data Scientist II (multiple positions available)Location: Dallas, TX Multiple Positions Available:1. Assist customers to deliver Machine Learning (ML) and Deep Learning (DL) projects from beginning to end, by aggregating data, exploring data, building and validating predictive models, and deploying completed models to deliver business impact to the organization;2. Apply understanding of the customer’s business need and guide them to a solution using AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances;3. Use Deep Learning frameworks like MXNet, PyTorch, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our customers build DL models;4. Research, design, implement and evaluate novel computer vision algorithms and ML/DL algorithms;5. Work with data architects and engineers to analyze, extract, normalize, and label relevant data;6. Work with DevOps engineers to help customers operationalize models after they are built;7. Assist customers with identifying model drift and retraining models;8. Research and implement novel ML and DL approaches, including using FPGA;9. Develop computer vision and machine learning methods and algorithms to address real-world customer use-cases; and10. Design and run experiments, research new algorithms, and work closely with engineers to put algorithms and models into practice to help solve customers' most challenging problems.11. Approximately 15% domestic and international travel required.12. Telecommuting benefits are available.#0000
US, WA, Seattle
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Manager III, Data ScienceLocation: Bellevue, WashingtonPosition Responsibilities:Manage a team of data scientists working to build large-scale, technical solutions to increase effectiveness of Amazon Fulfillment systems. Define key business goals and map them to the success of technical solutions. Aggregate, analyze and model data from multiple sources to inform business decisions. Manage and quantify improvement in the customer experience resulting from research outcomes. Develop and manage a long-term research vision and portfolio of research initiatives, with algorithms and models that to be integrated in production systems. Hire and mentor junior is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, VA, Arlington
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist IILocation: Arlington, VirginiaPosition Responsibilities:Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, IL, Chicago
MULTIPLE POSITIONS AVAILABLECompany: AMAZON.COM SERVICES LLCPosition Title: Data Scientist ILocation: Chicago, IllinoisPosition Responsibilities:Build the core intelligence, insights, and algorithms that support the real estate acquisition strategies for Amazon physical stores. Tackle cutting-edge, complex problems such as predicting the optimal location for new Amazon stores by bringing together numerous data assets, and using best-in-class modeling solutions to extract the most information out of them. Work with business stakeholders, software development engineers, and other data scientists across multiple teams to develop innovative solutions at massive is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000
US, WA, Bellevue
How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you measure sellers' response to /causal impacts of capacity control policies we implemented at Amazon using the state-of-the-art econometric techniques? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? How do you design and evaluate seller assistance to drive their success? If these type of questions get your mind racing, we want to hear from you.Supply Chain Optimization Technologies (SCOT) optimizes Amazon’s global supply chain end to end and build systems to deliver billions of products to our customers’ doorsteps faster every year while saving hundreds of millions of dollars using economics, operational research, machine learning, and scalable distributed software on the Cloud. Fulfillment by Amazon (FBA) is an Amazon service for our marketplace third party sellers, where our sellers leverage our world-class facilities and provide customers Prime delivery promise on all their goods.We are looking for the next outstanding economist to join our interdisciplinary team of data scientists, research scientists, applied scientists, economists. The ideal candidate combines econometric acumen with strong business judgment. You have versatile modeling skills and are comfortable extracting insights from observational and experimental data. You translate insights into action through proofs-of-concept and partnerships with engineers and data scientists to productionize. You are excited to learn from and alongside seasoned analysts, scientists, engineers, and business leaders. You are an excellent communicator and effectively translate business ideas and technical findings into business action (and customer delight).Key job responsibilitiesProvide data-driven guidance and recommendations on strategic questions facing the FBA leadershipDesign and implement V0 models and experiments to kickstart new initiatives, thinking, and drive system-level changes across AmazonHelp build a long-term research agenda to understand, break down, and tackle the most stubborn and ambiguous business challengesInfluence business leaders and work closely with other scientists at Amazon to deliver measurable progress and change
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve the employee and manager experience at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science!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 seeking a senior Applied Scientist with expertise in more than one or more of the following areas: machine learning, natural language processing, computational linguistics, algorithmic fairness, statistical inference, causal modeling, reinforcement learning, Bayesian methods, predictive analytics, decision theory, recommender systems, deep learning, time series modeling. In this role, you will lead and support research efforts within all aspects of the employee lifecycle: from candidate identification to recruiting, to onboarding and talent management, to leadership and development, to finally retention and brand advocacy upon exit.The ideal candidate should have strong problem-solving skills, excellent business acumen, the ability to work independently and collaboratively, and have an expertise in both science and engineering. The ideal candidate is not methods-driven, but driven by the research question at hand; in other words, they will select the appropriate method for the problem, rather than searching for questions to answer with a preferred method. The candidate will need to navigate complex and ambiguous business challenges by asking the right questions, understanding what methodologies to employ, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders).About the teamWe are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
US, WA, Bellevue
Job summaryThe Global Supply Chain-ACES organization aims to raise the bar on Amazon’s customer experience by delivering holistic solutions for Global Customer Fulfillment that facilitate the effective and efficient movement of product through our supply chain. We develop strategies, processes, material handling and technology solutions, reporting and other mechanisms, which are simple, technology enabled, globally scalable, and locally relevant. We achieve this through cross-functional partnerships, listening to the needs of our customers and prioritizing initiatives to deliver maximum impact across the value chain. Within the organization, our Quality team balances tactical operation with operations partners with global engagement on programs to deliver improved inventory accuracy in our network. The organization is looking for an experienced Principal Research Scientist to partner with senior leadership to develop long term strategic solutions. As a Principal Scientist, they will lead critical initiatives for Global Supply Chain, leveraging complex data analysis and visualization to:a. Collaborate with business teams to define data requirements and processes;b. Automate data pipelines;c. Design, develop, and maintain scalable (automated) reports and dashboards that track progress towards plans;d. Define, track and report program success metrics.e. Serve as a technical science lead on our most demanding, cross-functional projects.