Astro’s Intelligent Motion brings state-of-the-art navigation to the home

“Body language” and an awareness of social norms help Amazon’s new household robot integrate gracefully into the home.

At a virtual event today, Amazon’s senior vice president for devices, Dave Limp, unveiled his organization’s new lineup of devices, which included Astro, a household robot with home monitoring and Alexa.

Building a robot that can move intelligently around your home is no easy task. When building self-driving cars or robots for industrial applications, you can predefine high-definition maps of the environments they will encounter on the open road or factory floor. But in the home, nothing is predefined, with frequently rearranged furniture and belongings and people and pets always in motion.

When we set out to build Astro, we knew we wanted its motion to be intuitive and graceful, and we wanted it to be able to interact naturally with humans. That meant that we had to account for the dynamism of the home when deciding on Astro’s design, its sensor configuration, its algorithms, and the speed at which it moves. In addition, we had to deliver Astro at a consumer-accessible price point with a highly optimized suite of sensors and sufficient processing power, when the sensors and processors for other industrial robots that operate at similar speeds can cost thousands of dollars.

So how did we create Intelligent Motion for Astro? 

Astro

Perception and mapping

To be able to move around your home, Astro needs to effectively map its surroundings and understand where it is at any given point: this is perception. Astro’s computer vision system observes the world with both visible and infrared light, which gives it robust perception in dynamic environments and varying lighting conditions. As it perceives where it is in a space, Astro uses its suite of navigation and obstacle sensors as inputs to its on-device simultaneous localization and mapping (SLAM) and obstacle avoidance systems. 

The navigation sensors help identify the positions of key landmarks in 3-D space for the SLAM system, such as corners of tables and doorframes, so that Astro can figure out where it is relative to these landmarks. Astro builds a map of the relative positions of these sparse landmarks when it explores your home and then uses the landmarks to update its location as it moves through the home. 

The obstacle sensors help Astro build a detailed map of its immediate surroundings, capturing the distance to obstacles like couches, chairs, walls, and stairs (see figure below). Astro then uses its knowledge of its position from SLAM and its map of obstacles to path plan and interact with its environment, performing complex tasks such as exploring the home and determining boundaries between spaces, following and approaching people, and figuring out where to hang out. We’ll dive deeper into Intelligent Motion’s SLAM and obstacle avoidance systems in a future science blog post.

Astro point cloud.png
Astro’s Intelligent Motion algorithms build a depth map of Astro's surroundings for mapping and path planning.

Real-time planning

Intelligent Motion is all about having Astro make decisions quickly and autonomously. Homes are ever-changing and full of moving obstacles. For that reason, Astro’s knowledge of its world is rarely perfect, so its navigation system has to be able to handle variability. 

Option testing.png
Astro’s path-planning algorithm tests hundreds of options in real time. Blue arrows indicate longer-range route guidance; colored lines represent options for close-range trajectories within the next three seconds. The colors represent scoring of the trajectories across many weighted factors.

As Astro navigates the home, the Intelligent Motion system generates several hundred potential paths several times a second, evaluates each of them, and then makes a determination on how to move. This process factors in the possibility of changes in the environment (e.g., a book bag dropped on the floor), the desired smoothness of the motion path, and the potential for encountering obstacles.

Astro weighs how each choice contributes toward achieving its current goal, whether that’s reaching a person or heading back to its charger. Astro keeps repeating this process while it is navigating, intelligently optimizing based on its latest knowledge of its world. This approach involves novel methods for dimensionality reduction and probabilistic planning that advance the state of the art in the field of consumer robotics. We’ll also cover this more in a future science blog post.

Body language and communication of intent

Controlling speed, acceleration, and the curvature of Astro’s path are important for making sure Astro can move safely, gracefully, and confidently through the home, but Astro needs to do even more when it interacts with humans. Human-robot interaction (HRI) is a rapidly growing area of research, one that Amazon has invested in in its study of consumer robotics. 

Astro builds trust with customers by moving with predictable behaviors, such as signaling its intents through body language. People and pets do the same thing — signaling, for instance, how they plan to move with a slight turn of the head, change in shoulder angle, or change in eye direction. These are signals people pick up on without even realizing it. 

Emulating these patterns, Astro uses natural changes in head angle as it moves around, indicating which way it is going to turn, pointing at the person it is approaching, and more. When we tested these features, the difference in customer experience with and without them was clear. A simple signal executed via well-coordinated screen and body movements is a powerful tool for communicating intent in real time and making Astro’s behavior more natural.

Moving at humanlike speeds

Astro’s ability to interact naturally with people helps make it even more useful in customers’ homes. Astro can tell when an obstacle is a person and make decisions about how to interact appropriately. To do this, Astro has to operate at human-scale speeds and have an awareness of social norms. 

Socially appropriate distance.png
When following a person, Astro maintains a socially appropriate distance.

For example, when Astro approaches a person, Intelligent Motion uses computer vision signals like the approximate position of that person relative to Astro and the direction the person is facing, the stored map for the area, and other inputs from Astro’s navigation and depth sensors to plan a smooth, graceful path that will enable Astro to end up in front of the person, in the person’s line of sight, at a socially appropriate distance. 

If Astro is following a person, Intelligent Motion helps Astro follow at a comfortable strolling pace for an adult, maintaining a socially appropriate distance, and estimating where that person goes when moving out of view so that Astro can move to a point where the person can be seen and followed again. Astro can determine when an obstacle it detects is a person and follow that obstacle instead of avoiding it, even when it moves in and out of Astro’s field of view. This approach involves dynamic obstacle recognition and tracking, path planning, proxemics, and HRI that we’re excited to share more about soon. 

Recovering from difficult situations

Despite its navigation prowess, Astro will still encounter situations that require it to problem-solve to avoid the need for human intervention. Intelligent Motion includes a set of recovery behaviors that can help when Astro encounters challenges to normal path planning, such as a narrow path that is currently blocked. 

To continue with its task in the face of a blocked path, Astro might try backing up until there is enough space to turn around. As part of this process, Astro also determines when it is time ask for help. We know from our internal testing that people don’t mind occasionally helping Astro, though we have also learned that people have limited patience for a robot that gives up too often and is always asking for help. 

Navigation.png
Astro heads for a gap but is blocked, so the planner calculates new waypoints (blue arrows), and the recovery planner finds a way out and onto the new path.

How Intelligent Motion is designed to protect customer privacy

Moving and reacting quickly requires a very fast system, making local processing of data essential. The raw data from the navigation and obstacle sensors is locally processed into a distance measurement and then discarded, without being sent to the cloud.

When Astro saves a new map at the completion of exploration, information derived from its navigation and depth sensors, including a copy of the 2-D obstacle map, is sent to the cloud, where a map of the home is created and stored. A rendering of the map can then be shown in the Astro app. 

This map contains derived information such as the location of walls, rooms, boundaries, furniture, and objects, plus related data such as customer-provided room names. Map data is encrypted in transit to the cloud, where it is securely stored with 256-bit keys, an industry standard for secure encryption. For more information about the way Astro protects customer privacy, visit amazon.com/astroprivacy

What's next?

Astro is Amazon’s first household robot to use Intelligent Motion to gracefully and intuitively interact with people, help customers monitor their homes, bring the power of Alexa to them, and give them back time in their busy lives. 

This is just the beginning for Intelligent Motion, with its navigation and HRI capabilities. We have exciting plans for advancing the science and engineering of Intelligent Motion so that it will improve over time at navigating in homes and serving customers’ needs. We also expect to learn a lot from our customers, who have never had a product quite like Astro in their homes before. Astro’s Intelligent Motion is a brand-new experience that we can’t wait for you to try, and we’re excited to have you join us on the journey.

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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