An overhead shot shows the Robin robot arm lifting a package via suction cups
Amazon Robotics researchers created a new learning system called Janus, which provides a robust framework for retraining Robin robotic arms and represents a major step toward development of a continual learning platform that will help Amazon retrain all its robots in the future.

Amazon’s Janus framework lifts continual learning to the next level

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

Watching items move down a conveyor belt toward a Robin robot arm at an Amazon fulfillment center is a great place to learn about the role continual learning plays in robotics.

The packages Robin encounters can include boxes, cylinders, and padded mailers of different shapes, sizes, and colors. Each group is different. Robin’s computer-vision system must make sense of them all by segmenting those packages into individual elements.

Related content
An advanced perception system, which detects and learns from its own mistakes, enables Robin robots to select individual objects from jumbled packages — at production scale.

This is something a child can do instinctively. But it took months of training for the Robin robotic arm to distinguish among the different package types.

The types of packages we ship and the distribution of these packages changes frequently. Our models need to adapt to these changes while maintaining high performance. To do this, we require continual learning.
Cassie Meeker

Scientists initially trained Robin to identify the different packages utilizing supervised learning, which graded the vision system’s accuracy as it tried to segment piles of packages from tens of thousands of images. Eventually, the system’s accuracy improved to the point where the robotics arms could be deployed in Amazon fulfillment centers.

Yet, there was a catch — the packages that Amazon delivers arrive in a constantly shifting variety of shapes and sizes.

“The problem with machine learning is that models must adapt to continually changing data conditions,” says Cassie Meeker, an Amazon Robotics applied scientist who is an expert user of Amazon’s continuous learning system. “Amazon is a global company — the types of packages we ship and the distribution of these packages changes frequently. Our models need to adapt to these changes while maintaining high performance. To do this, we require continual learning.”

To get there, Meeker’s team created a new learning system—a framework powerful and smart enough to adapt to distribution shifts in real time.

The framework, called Janus, automates some aspects of the retraining process. Named after the Roman god of transitions, Janus provides a robust framework for retraining Robin robotic arms and represents a major step toward development of a continual learning platform that will help Amazon retrain all its robots in the future.

A complex challenge

The concept of continual learning appears deceptively simple, says Hank Chen, an Amazon machine learning engineer who has worked on Janus since its inception. Robin, whose accuracy generally tops 99%, encounters some unexpected packaging. Then, via continual learning, it adapts to account for that. But the challenge is far more complex than that.

The first hurdle involves deciding which anomalous events require retraining. Chen breaks these into two different classes. The first involves the robot’s environment. Perhaps a light failed and it is too dark to identify packages or maybe a camera was knocked out of focus. These types of anomalies are fairly easy to identify and technicians can usually fix them quickly.

Robin sorts packages

The second type of anomaly is informational.

“These events happen when something changes,” Chen says. “We might have a new package type, holiday art on packages, or a hot new toy with transparent packaging. Recently, our European fulfillment centers began using black bags and that threw Robin for a loop. These are the types of novel data we want to learn from and model.”

Amazon trains its models on images featuring those packages. Once they are identified, the continual learning team annotates the novel images. This involves labeling the location, boundaries, shape, and classification of the packages in the scene.

When the team gathers enough annotated images, it can begin to retrain Robin’s models with fresh data, maintaining and even improving Robin’s ability to recognize both known and new packages.

Efficiently training models, however, requires a wide variety of examples.

“When we get a good initial raw image, we do what is called augmentation,” explains Larry Li, a software development manager who manages the Janus team. “We shrink the image, flip it, rotate it, make it darker or brighter, discolor it, make it blurry, and juxtapose with other images. This multiplies every image many times, giving the large number of images we need to train our model.”

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

To ensure that new data does not reduce the accuracy of existing models, Amazon tests retrained models on historical data to see if the machine retains — or, better still, improves — its level of performance. If the model succeeds, it moves to live testing.

This takes place on a special station set up for testing prototype robots. Researchers create piles of test packages to ensure the robot can handle them all. If it can, they beta test it on one or two lines within the company’s fulfillment centers. Only after a robot proves its performance does Amazon deploy it more broadly.

Automating processes

Simultaneously capturing novel events, categorizing them based on recurrence, annotating images, creating training decks, and performing model training is a lot to manage — Janus has been designed to automate these processes.

“We want to automate how we retrain our models in response to changing conditions and new data,” Meeker says.

Janus, for example, automatically monitors when robots such as Robin encounter novel events.

“If a human was uncertain about something, they could tell us what caused that confusion,” Meeker notes. “But a robot can’t tell us what the problem was. Instead, we have to use other metrics to figure out when and why a model is not confident.

Robin's advanced perception system

“When presented with a cluttered scene, for example, Robin’s model will segment the scene into individual packages — each box, padded mailer, et cetera is individually labeled and the package boundaries are marked. If the robot fails to pick up the package, drops the package, or picks up a different package, we can look at how the model segmented the scene to identify the problem.”

Janus automatically identifies problematic packages for annotation. Those annotations make it easier to identify and rank the packages most likely to cause Robin challenges.

Performing these tasks in real time is computationally intensive. At the same time, Amazon’s fleet of Robin robots is growing. To minimize computing overhead, continual learning relies on Amazon Web Services to tap functions from the cloud on an as-needed basis.

Related content
Scientists and engineers are developing a new generation of simulation tools accurate enough to develop and test robots virtually.

“We leverage AWS components to create an ‘assembly line’ for computer learning,” Li says. “We also use a novel image detector to detect significant changes in our targets and environment. When those conditions happen, it triggers a batch job to sample the raw images and preserve them for potential retraining.”

Reinforcement learning

Ultimately, Chen says, the continual learning team wants to transform Janus from a set of code libraries into an integrated service that any user could pull off the shelf and plug into their robot.

“Once they have the model, it would look for anomalies, pick out the most frequent novel events, and learn from them,” he says.

Humans, for example, might move a pile of packages around to pick one up, but how do we capture that capability with a robot and not slow down the line? Reinforcement learning might give us a way to do this.
Larry Li

Janus may also evolve to embrace reinforcement learning.

“In reinforcement learning, it is up to the model to explore the possibilities and find the proper solution,” Li explains. “The results are markedly different than supervised learning because there is a closer coupling between perception and action. The actions a robot takes can be used to generate best outcomes. Humans, for example, might move a pile of packages around to pick one up, but how do we capture that capability with a robot and not slow down the line? Reinforcement learning might give us a way to do this.”

Related content
Zoox principal software engineer Olivier Toupet on company’s autonomous robotaxi technology

Robin’s ability to interpret images is already very good, Meeker says. Her group now wants to extend those capabilities to other robots.

“We want to create universal models that can segment packages with less training data,” Meeker explains. “We do this by pre-training a model with a large dataset collected from across different environments, different tasks and different backgrounds. Then we fine tune the model with small amounts of data from a new environment. With a relatively small amount of data, you can get high segmentation performance. A continuous learning framework like Janus allows us to scale our universal model, so we can train across many different tasks and environments.”

Related content

IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.