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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The Data Intelligence team is a new function within Amazon Customer Service (CS). We own the end-to-end process of defining, building, implementing, and monitoring a comprehensive data strategy. We also develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), Ontology, and Natural Language Processing (NLP) to enhance customer service associate and customer experiences. As an Applied Scientist, you'll own the definition and implementation of customer-focused, AI-driven innovation in Amazon Customer Service globally, leveraging GenAI, ML, and/or NLP to transform complex business requirements and customer needs into innovative technology solutions. Your expertise will be key in shaping data-driven strategies and addressing complex data challenges. With your expertise in AI, text analysis, embeddings, language modeling, and generation, you'll design and develop scalable AI-powered technology solutions, prioritize initiatives, drive data-driven insights, and deliver business impact. This position will advance applied science best practices, leverage data and AI to drive customer experience improvements, and set new global standards for customer experience. This role requires you to work with a cross-functional team, including scientists, engineers, and product managers, to develop scalable and maintainable AI solutions for both structured and unstructured data. The ideal candidate has strong technical skills in AI techniques (e.g., automated reasoning, reasoning, planning, knowledge representation), excellent written documentation skills, and experience with big data technologies. Success in this role requires combining deep business knowledge with hands-on technical skills to solve customer problems and address complex technical challenges. Key job responsibilities - Develop innovative solutions to complex problems (e.g., Automated Reasoning for Trusted AI-Enabled Customer Service). - Apply technical expertise to implement novel algorithms and modeling solutions, in collaboration with other scientists and engineers. - Analyze data and define metrics to identify actionable insights and measure improvements in customer experience. - Communicate results and insights to both technical and non-technical audiences through written reports, presentations, and internal/external publications. - Collaborate with product management and engineering teams to integrate and optimize models in production systems. A day in the life A typical day as an Applied Scientist in the Data Intelligence team involves combining business expertise with hands-on problem-solving in ML and AI. The role encompasses tackling complex data initiatives, ensuring alignment with customer needs and business objectives, and translating business requirements into practical AI-driven solutions. Working collaboratively with cross-functional teams, this position involves designing and enhancing AI models, focusing on efficiency, precision, and scalability. Daily activities include ensuring data quality, monitoring model performance, and generating actionable insights from vast amounts of information. Each day presents opportunities to resolve complex technical challenges, advance important AI projects, and conceive innovative ways to leverage data in transforming the customer experience. About the team The Data Intelligence team is a new function within Amazon Customer Service. We develop and apply Generative Artificial Intelligence (GenAI), Machine Learning (ML), and Natural Language Processing (NLP) techniques to enhance customer service associate and customer experiences.
US, CA, East Palo Alto
As part of the AWS Solutions organization, we have a vision to provide business applications, leveraging Amazon’s unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers’ businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. We blend vision with curiosity and Amazon’s real-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no-brainers to buy and easy to use. Key job responsibilities Everyone on the team needs to be entrepreneurial, wear many hats and work in a highly collaborative environment that’s more startup than big company. We’ll need to tackle problems that span a variety of domains: computer vision, image recognition, machine learning, real-time and distributed systems. As an Applied Scientist, you will help solve a variety of technical challenges and mentor other scientists. You will tackle challenging, novel situations every day and given the size of this initiative, you’ll have the opportunity to work with multiple technical teams at Amazon in different locations. You should be comfortable with a degree of ambiguity that’s higher than most projects and relish the idea of solving problems that, frankly, haven’t been solved at scale before - anywhere. Along the way, we guarantee that you’ll learn a ton, have fun and make a positive impact on millions of people. A key focus of this role will be developing and implementing advanced visual reasoning systems that can understand complex spatial relationships and object interactions in real-time. You'll work on designing autonomous AI agents that can make intelligent decisions based on visual inputs, understand customer behavior patterns, and adapt to dynamic retail environments. This includes developing systems that can perform complex scene understanding, reason about object permanence, and predict customer intentions through visual cues. About the team Just Walk Out (JWO) is a new kind of store with no lines and no checkout—you just grab and go! Customers simply use the Amazon Go app to enter the store, take what they want from our selection of fresh, delicious meals and grocery essentials, and go! Our checkout-free shopping experience is made possible by our Just Walk Out Technology, which automatically detects when products are taken from or returned to the shelves and keeps track of them in a virtual cart. When you’re done shopping, you can just leave the store. Shortly after, we’ll charge your account and send you a receipt. Check it out at amazon.com/go. Designed and custom-built by Amazonians, our Just Walk Out Technology uses a variety of technologies including computer vision, sensor fusion, and advanced machine learning. Innovation is part of our DNA! Our goal is to be Earths’ most customer centric company and we are just getting started. We need people who want to join an ambitious program that continues to push the state of the art in computer vision, machine learning, distributed systems and hardware design.
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through innovative foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.