How Amazon Robotics researchers are solving a “beautiful problem”

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

The rate of innovation in machine learning is simply off the chart — what is possible today was barely on the drawing board even a handful of years ago. At Amazon, this has manifested in a robotic system that can not only identify potential space in a cluttered storage bin, but also sensitively manipulate that bin’s contents to create that space before successfully placing additional items inside — a result that, until recently, was impossible.

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This journey starts when a product arrives at an Amazon fulfillment center (FC). The first order of business is to make it available to customers by adding it to the FC's available inventory.

The stowing process

In practice, this means picking it up and stowing it in a storage pod. A pod is akin to a big bookcase, made of sturdy yellow fabric, that comprises up to 40 cubbies, known as bins. Each bin has strips of elastic across its front to keep the items inside from falling out. These pods are carried by a wheeled robot, or drive unit, to the workstation of the Amazon associate doing the stowing. When the pod is mostly full, it is wheeled back into the warehouse, where the items it contains await a customer order.

Stowing is a major component of Amazon’s operations. It is also a task that seemed an intractable problem from a robotic automation perspective, due to the subtlety of thought and dexterity required to do the job.

Picture the task. You have an item for stowing in your hand. You gauge its size and weight. You look at the array of bins before you, implicitly perceiving which are empty, which are already full, which bins have big chunks of space in them, and which have the potential to make space if you, say, pushed all the items currently in the bin to one side. You select a bin, move the elastic out of the way, make room for the item, and pop it in. Job done. Now repeat.

“Breaking all existing industrial robot thinking”

This stow task requires two high-level capabilities not generally found in robots. One, an excellent understanding of the three-dimensional world. Two, the ability to manipulate a wide range of packaged but sometimes fragile objects — from lightbulbs to toys — firmly, but sensitively: pushing items gently aside, flipping them up, slotting one item at an angle between other items and so on.

A simulation of robotic stowing

For a robotic system to stand a chance at this task, it would need intelligent visual perception, a free-moving robot arm, an end-of-arm manipulator unknown to engineering, and a keen sense of how much force it is exerting. In short: good luck with that.

“Stow fundamentally breaks all existing industrial robotic thinking,” says Siddhartha Srinivasa, director of Amazon Robotics AI. “Industrial manipulators are typically bulky arms that execute fixed trajectories very precisely. It’s very positional.”

When Srinivasa joined Amazon in 2018, multiple robotics programs had already attempted to stow to fabric pods using stiff positional manipulators.

“They failed miserably at it because it's a nightmare. It just doesn't work unless you have the right computational tool: you must not think physically, but computationally.”

Srinivasa knew the science for robotic stow didn’t exist yet, but he knew the right people to hire to develop it. He approached Parker Owan as he completed his PhD at the University of Washington.

A “beautiful problem”

Parker Owan, Robotics AI senior applied scientist, poses next to a robotic arm and in front of a yellow soft sided storage pod
Parker Owan, Robotics AI senior applied scientist

“At the time I was working on robotic contact, imitation learning, and force control,” says Owan, now a Robotics AI senior applied scientist. “Sidd said ‘Hey, there’s this beautiful problem at Amazon that you might be interested in taking a look at’, and he left it at that.”

The seed was planted. Owan joined Amazon, and then in 2019 dedicated himself to the stow challenge.

“I came at it from the perspective of decision-making algorithms: the perception needs; how to match items to the appropriate bin; how to leverage information of what's in the bin to make better decisions; motion planning for a robot arm moving through free space; and then actually making contact with products and creating space in bins.”

Aaron Parness, Robotics AI senior manager of applied science, poses near a robotic arm
Aaron Parness, Robotics AI senior manager of applied science

About six months into his exploratory work, Owan was joined by a small team of applied scientists, and hardware expert Aaron Parness, now a Robotics AI senior manager of applied science. Parness admits he was skeptical.

“My initial reaction was ‘Oh, how brave and naïve that this guy, fresh out of his PhD, thinks robots can deal with this level of clutter and physical contact!’”

But Parness was quickly hooked. “Once you see how the problem can be broken down and structured, it suddenly becomes clear that there's something super useful and interesting here.”

“Uncharted territory”

From a hardware perspective, the team needed to find a robot arm with force feedback. They tried several, before the team landed on an effective model. The arm provides feedback hundreds of times per second on how much force it is applying and any resistance it is meeting. Using this information to control the robot is called compliant manipulation.

“We knew from the beginning that we needed compliant manipulation, and we hadn't seen anybody in industry do this at scale before,” says Owan. “It was uncharted territory.”

Parness got to work on the all-important hardware. The problem of moving the elastics aside to stow an item was resolved using a relatively simple hooking system.

How the band separator works

The end-of-arm tool (EOAT) proved to be a next-level challenge. One reason that stowing is difficult for robots is the sheer diversity of items Amazon sells, and their associated packaging. You might have an unpumped soccer ball next to a book, next to a sports drink, next to a T-shirt, next to a jewelry box. A robot would need to handle this level of variety. The EOAT evolved quickly over two years, with multiple failures and iterations.

Paddles grip an array of items

“In the end, we found that gently squeezing an item between two paddles was the more stable way to hold items than using suction cups or mechanical pinchers,” says Parness.

However, the paddle set up presented a challenge when trying to insert held items into bins — the paddles kept getting in the way. Parness and his growing team hit upon an alternative: holding the item next to a bin, before simultaneously opening the paddles and using a plunger to push the item in. This drop-and-push technique was prone to errors because not all items reacted to it in the same way.

The EOAT’s next iteration saw the team put miniature conveyor belts on each paddle, enabling the EOAT to feed items smoothly into the bins without having to enter the bin itself.

The miniature conveyor belt works to bring an item to its designated bin

“With that change, our stowing success rate jumped from about 80% to 99%. That was a eureka moment for us — we knew we had our winner,” says Parness.

Making space with motion primitives

The ability to place items in bins is crucial, but so is making space in cluttered bins. To better understand what would be required of the robot system, the team closely studied how they performed the task themselves. Owan even donned a head camera to record his efforts.

The team was surprised to find that the vast majority of space-making hand movements within a fabric bin could be boiled down to four types or “motion primitives”. These include a sideways sweep of the bin’s current contents, flipping upright things that are lying flat, stacking, and slotting something at an angle into the gap between other items.

The process of making space

The engineers realized that the EOAT’s paddles could not get involved with this bin-manipulation task, because they would get in the way. The solution, in the end, was surprisingly simple: a thin metal sheet that could extend from the EOAT, dubbed “the spatula”. The extended spatula can firmly, but sensitively, push items to one side, flip them up, and generally be used to make room in a bin, before the paddles eject an item into the space created.

But how does the system know how full the pod’s bins are, and how does it decide where, and how, it will make space for the next item to be stowed? This is where visual perception and machine learning come into play.

Deciding where to attempt to stow an item requires a good understanding of how much space, in total, is available in each fabric bin. In an ideal world, this is where 3D sensor technologies such as LiDAR would be used. However, because the elastic cords across the front of every bin partially blocks the view inside, this option isn’t feasible.

A robot arm executes motion primitives

Instead, the system’s visual perception is based on cameras pointed at the pod that feed their image data to a machine learning system. Based on what it can see of each bin’s contents, the system “erases” the elastics and models what is lying unseen in the bin, and then estimates the total available space in each of the pod’s bins.

Often there is space available in a cluttered bin, but it is not contiguous: there are pockets of space here and there. The ML system — based in part on existing models developed by the Amazon Fulfillment Technologies team — then predicts how much contiguous space it can create in each bin, given the motion primitives at its disposal.

How the perception system "sees" available space

“These primitives, each of which can be varied as needed, can be chained in infinitely many ways,” Srinivasa explains. “It can, say, flip it over here, then push it across and drop the item in. Humans are great at identifying these primitives in the first place, and machine learning is great at organizing and orchestrating them.”

When the system has a firm idea of the options, it considers the items in its buffer — an area near the robot arm’s gantry in which products of various shapes and sizes wait to be stowed — and decides which items are best placed in which bins for maximum efficiency.

“For every potential stow, the system will predict its likelihood of success,” says Parness. “When the best prediction of success falls to about 96%, which happens when a pod is nearly full, we send that pod off and wheel in a new one.”

“Robots and people work together”

At the end of summer 2021, with its potential feasibility and value becoming clearer, the senior leadership team at Amazon gave the project their full backing.

“They said ‘As fast as you can go; whatever you need’. So this year has been a wild, wild ride. It feels like we’re a start-up within Amazon,” says Parness, who noted the approach has significant advantages for FC employees as well.

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“Robots and people work together in a hybrid system. Robots handle repetitive tasks and easily reach to the high and low shelves. Humans handle more complex items that require intuition and dexterity. The net effect will be more efficient operations that are also safer for our workers.”

Prototypes of the robotic stow workstation are installed at a lab in Seattle, Washington, and another system has been installed at an FC in Sumner, Washington, where it deals with live inventory. Already, the prototypes are stowing items well and showcasing the viability of the system.

“And there are always four or five scientists and engineers hovering around the robot, documenting issues and looking for improvements,” says Parness.

Stow will be the first brownfield automation project, at scale, at Amazon. We're enacting a future in which robots and humans can actually work side by side without us having to dramatically change the human working environment.
Siddhartha Srinivasa

This year, in a stowing test designed to include a variety of challenging product attributes — bagged items, irregular items with an offset center of gravity, and so on — the system successfully stowed 94 of 95 items. Of course, some items can never be stowed by this system, including particularly bulky or heavy products, or cylindrical items that don’t behave themselves on conveyor belts. The team’s ultimate target is to be able to stow 85% of products stocked by a standard Amazon FC.

“Interacting with chaotic arrangements of items, unknown items with different shapes and sizes, and learning to manipulate them in intelligent ways, all at Amazon scale — this is ground-breaking,” says Owan. “I feel like I’m at ground zero for a big thing, and that’s what makes me excited to come to work every day.”

“Stow will be the first brownfield automation project, at scale, at Amazon,” says Srinivasa. “Surgically inserting automation into existing buildings is very challenging, but we're enacting a future in which robots and humans can actually work side by side without us having to dramatically change the human working environment.

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"One of the advantages of the type of brownfield automation we do at Robotics AI is that it’s minimally disruptive to the process flow or the building space, which means that our robots can truly work alongside humans," Srinivasa adds. "This is also a future benefit of compliant arms as they can, via software and AI, be made safer than industrial arms.”

Robots and humans working side by side is key to the long-term expansion of this technology beyond retail, says Parness.

“Think of robots loading delicate groceries or, longer term, loading dishwashers or helping people with tasks around the house. Robots with a sense of force in their control loop is a new paradigm in compliant-robotics applications.”

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AWS AI is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology and code generation. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU
US, VA, Arlington
Amazon launched the Generative AI Innovation Center (GenAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GenAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GenAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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. Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NY, USA | San Francisco, CA, USA | San Jose, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
US, VA, Arlington
Machine learning (ML) has been strategic to Amazon from the early years. We are pioneers in areas such as recommendation engines, product search, eCommerce fraud detection, and large-scale optimization of fulfillment center operations. The Generative AI team helps AWS customers accelerate the use of Generative AI to solve business and operational challenges and promote innovation in their organization. As an applied scientist, you are proficient in designing and developing advanced ML models to solve diverse challenges and opportunities. You will be working with terabytes of text, images, and other types of data to solve real-world problems. You'll design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for talented scientists capable of applying ML algorithms and cutting-edge deep learning (DL) and reinforcement learning approaches to areas such as drug discovery, customer segmentation, fraud prevention, capacity planning, predictive maintenance, pricing optimization, call center analytics, player pose estimation, event detection, and virtual assistant among others. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. Key job responsibilities The primary responsibilities of this role are to: - Design, develop, and evaluate innovative ML models to solve diverse challenges and opportunities across industries - Interact with customer directly to understand their business problems, and help them with defining and implementing scalable Generative AI solutions to solve them - Work closely with account teams, research scientist teams, and product engineering teams to drive model implementations and new solution About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred 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. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Atlanta, GA, USA | Austin, TX, USA | Houston, TX, USA | New York, NJ, USA | New York, NY, USA | San Francisco, CA, USA | Santa Clara, CA, USA | Seattle, WA, USA
CN, Shanghai
亚马逊云科技上海人工智能实验室OpenSearch 研发团队正在招募应用科学实习生-多模态检索与生成方向实习生。OpenSearch是一个开源的搜索和数据分析套件, 它旨在为数据密集型应用构建解决方案,内置高性能、开发者友好的工具,并集成了强大的机器学习、数据处理功能,可以为客户提供灵活的数据探索、丰富和可视化功能,帮助客户从复杂的数据中发现有价值的信息。OpenSearch是现有AWS托管服务(AWS OpenSearch)的基础,OpenSearch核心团队负责维护OpenSearch代码库,他们的目标是使OpenSearch安全、高效、可扩展、可扩展并永远开源。 点击下方链接查看申请手册获得更多信息: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 在这个实习期间,你将有机会: 1. 研究最新的搜索相关性人工智能算法。 2. 探索大模型技术在数据分析与可视化上的应用。 3. 了解主流搜索引擎Lucene的原理和应用。深入了解前沿自然语言处理技术和底层索引性能调优的结合。 4. 学习亚马逊云上的各种云服务。 5. 参与产品需求讨论,提出技术实现方案。 6. 与国内外杰出的开发团队紧密合作,学习代码开发和审查的流程。 We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN