NASA's Orion spacecraft shown splashing down in the Pacific Ocean, west of Baja California, at 9:40 a.m. PST Sunday, Dec. 11.
NASA's Orion spacecraft shown splashing down in the Pacific Ocean, west of Baja California, at 9:40 a.m. PST Sunday, Dec. 11.
NASA

The story behind how Amazon integrated Alexa into NASA’s Orion spacecraft

From physical constraints to acoustic challenges, learn how Amazon collaborated with NASA and Lockheed Martin to get Alexa to work in space.

In September 2018, Amazon’s principal solutions architect Philippe Lantin received a call from his manager.

“He said that there was something unique on the horizon, and that their team was being roped into a one-in-a-lifetime opportunity,” says Lantin.

This was no understatement: on the horizon was an opportunity for Amazon to collaborate with Lockheed Martin Space, and integrate Alexa into NASA’s Orion spacecraft. Orion is the first human-rated spacecraft to visit the moon in more than 40 years.

“NASA is trying to engage the public more as we enter this new era of space travel, where we are setting the stage for extra-planetary exploration,” says Lantin. “Given that over 100 million Alexa-enabled devices have already been sold, having Alexa answer questions like 'Alexa, how far to the moon?' and 'Alexa, how fast is Orion going?' is a great way to get people around the world involved in NASA’s missions.”

Setting up an Echo device on Earth is simple: all you need is a Wi-Fi connection and the Alexa app. However, things are far more complicated in space.

“We had several constraints we had to contend with,” says Lantin.
The Alexa team had to operate within a key physical constraint: the shape of the device. The contours of a smart speaker greatly influences it acoustics. To give just one example, the round shape of the Echo Dot offers a full cavity behind the woofer for a better bass response.

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However, when it came to NASA’s Orion spacecraft, Alexa’s acoustic engineers had to work with what was provided by Lockheed Martin and NASA.

“We were somewhat limited by the form factor, which was a small briefcase-like enclosure that was 1.5 feet by one foot and about five inches in depth.” says Lantin.

There were other physical constraints. Equipment developed for the mission had to be resilient to extreme shocks and vibrations, be at least minimally resistant to radiation emissions in space, and utilize highly specific and custom-built components such as power and data cables.

Limited Internet connectivity

The team also had to deal with issues related to the lack of Internet connectivity. Typically, Echo devices use on-device keyword spotting designed to detect when a customer says the wake word. This on-device buffer exists in temporary memory. After the wake word is detected, the device streams the audio to the cloud for speech recognition and natural language processing.

Orion components

“However, for the Orion mission, our ability to communicate with the Alexa cloud was severely constrained,” says Lantin. “NASA’s spacecraft uses the Deep Space Network to communicate with earth. The bandwidth available to us on the downlink connection is slightly better than dial-up modem speeds with latencies of up to five seconds. To further complicate matters, NASA prioritizes traffic for navigation and telemetry for the first payload — traffic for Alexa was consigned to the secondary payload.”

The team also wanted to demonstrate a fully autonomous experience, one that can be used in future missions where Earth connectivity is no longer a practical option for real-time communications. They used Alexa Local Voice Control to get around the limited internet connectivity. Alexa Local Voice control allows select devices to process voice commands locally, rather than sending information to the cloud.

Lantin says that while the team was motivated by demonstrating technology leadership and scientific innovation in a very challenging environment, the real motivator was making a difference in the lives of millions of customers at home on earth.

“At Amazon, we take pride in delivering customer-focused science,” says Lantin. “That was a huge motivator for us at every step along the way. Consider the innovations we drove to Alexa Local Voice Control. These improvements will allow people on earth to do so much more with Alexa in situations where they have limited or no Internet connectivity. Think about when you are in a car and passing through a tunnel, or driving to a remote camping site. You can do things like tune the radio, turn on the AC and continue to use voice commands, even if you have a feeble signal or no cellular connection.”

Lantin says that the acoustic innovations enabled for Orion will also translate directly into improved listening experiences for people interacting with the mission on earth.

Rohit Prasad, Alexa senior vice president and head scientist, on the initial collaboration with Lockheed Martin

“We are planning to have celebrities, politicians, STEM students and a variety of other personalities interacting with Alexa,” says Lantin. “ And so, we also spent a good deal of time thinking about what people might want to ask Alexa about during the mission.”

The nuances of acoustics aboard Orion

Scott Isabelle is a solutions architect at Amazon. Prior to Amazon, Isabelle was a distinguished member of the technical staff at Motorola, where among other projects, he developed systems for enhancing voice quality in mobile devices, methods for generating adaptive ringtones, and a two-microphone system for noise suppression.

“One of the most important things for a voice AI is being in an environment where it is able to pick up your voice,” says Isabelle.

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However, this is easier said than done on Orion, where the conical shape of the space capsule, and its metallic surfaces result in increased reverberation.

“The voice can keep bouncing around losing very little energy. This wouldn’t happen in a typical room where soft material like curtains and sofa cushions can absorb some of the sound. In the capsule, the reverberations off the metal surfaces can play up the wrong frequencies that are critical to automatic speech recognition. This can make it really difficult for Alexa to pick up wake word invocations. ”

Alexa also has to contend with increased noise levels aboard Orion.

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The ideal signal to noise ratio (SNR) for systems involving intelligent voice assistants is in the range 20 to 30 decibels (dB). To place this in context, a SNR of 35 dB is what you would find in a face-to-face conversation between two people standing one meter apart in a typical room (higher SNRs are better). However, the SNR onboard the Orion capsule can be much lower than 20 dB, posing an acoustic challenge.

To enhance the comfort of astronauts during crewed missions, NASA would ordinarily place acoustic blankets to damp down the reverberation in the hard-walled cabin, and some of the noise created by engines and pumps.

“However, because this is an uncrewed mission we have to work within an environment with more reverberation and noise than we would like,” says Isabelle.

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There’s another challenge that results from the lack of humans on board. For Orion, commands to Alexa have to be sent from ground control. The low-bandwidth connections utilized for the transmission can make it challenging to transmit voices at the wide range of frequencies essential for differentiating between sounds.

During a typical phone call, our voice is typically transmitted in the narrow band, which ranges from 300 HZ to 3,000 HZ. For Alexa to make out individual words aboard the noisier environment of the space capsule, the voice would have to be transmitted at 8,000 HZ.

“Voice commands from mission control are transmitted to Alexa via a speaker,” says Isabelle. “Flight-qualified speakers are typically designed for narrow-band communications. And so for this mission we were required to use a speaker that could operate in the flight environment.”

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The team relied on what Isabelle calls “brute force” to overcome these acoustic challenges.

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“We designed the speaker playback system to play at extremely loud volumes, which allowed us to increase the SNR to where we wanted it to be.”

The team also took advantage of the physical form factor of Alexa on board to overcome the challenges presented by the noisy environment. The speakers, the light ring and the microphones in the briefcase-like enclosure for Alexa are close to each other, which allows acoustic engineers to overcome some of the obstacles presented by the background noise and reverberation.

Finally, the team deployed two microphones in combination with an array processing algorithm. The latter combined the signals from the two microphones in a way that helps Alexa make sense of the commands being issued from mission control. Because the speakers and microphones are in fixed positions relative to each other — as opposed to a room, where people can be located in any number of locations — the algorithms could be more easily designed to distinguish between speech and the surrounding noise.

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While the Orion mission will not have any crew members on board, the initial mission will lay the groundwork for Alexa to be integrated into future crewed missions — to the moon, Mars, and beyond. Having Alexa onboard in these future missions would allow crew members to be more efficient in day-to-day tasks, and benefit from the comforts of having Alexa on board such as the ability to play relaxing music and to keep in touch with family and friends back home.

Future crewed missions would have their own unique set of challenges, where Alexa would have to respond to commands from astronauts, who might (literally) be free-floating at multiple points within the capsule. Isabelle and Lantin are already looking forward to overcoming the challenges enabled by crewed missions.

“For someone who grew up watching Star Trek, working on this project has been a dream come true,” says Lantin. “It’s great to be able to build the future. But it’s just as exciting to be able to draw on all of this great work, and be able to enjoy all these new Alexa capabilities during my next vacation, and my day-to-day life right here at home.”

Editor's note

This is a reprint of an article that initially ran on the Alexa Skills Kit Blog. To learn more about the technical innovations that helped get Alexa into space and some inspiring facts about the Artemis I mission, visit the Skills Kit blog.

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Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. Key job responsibilities - Design and implement whole body control methods for balance, locomotion, and dexterous manipulation - Utilize state-of-the-art in methods in learned and model-based control - Create robust and safe behaviors for different terrains and tasks - Implement real-time controllers with stability guarantees - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for loco-manipulation - Mentor junior engineer and scientists
US, CA, San Francisco
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. As an Applied Scientist, you will develop and improve machine learning systems that help robots perceive, reason, and act in real-world environments. You will leverage state-of-the-art models (open source and internal research), evaluate them on representative tasks, and adapt/optimize them to meet robustness, safety, and performance needs. You will invent new algorithms where gaps exist. You’ll collaborate closely with research, controls, hardware, and product-facing teams, and your outputs will be used by downstream teams to further customize and deploy on specific robot embodiments. Key job responsibilities As an Applied Scientist in the Foundations Model team, you will: - Leverage state-of-the-art models for targeted tasks, environments, and robot embodiments through fine-tuning and optimization. - Execute rapid, rigorous experimentation with reproducible results and solid engineering practices, closing the gap between sim and real environments. - Build and run capability evaluations/benchmarks to clearly profile performance, generalization, and failure modes. - Contribute to the data and training workflow: collection/curation, dataset quality/provenance, and repeatable training recipes. - Write clean, maintainable, well commented and documented code, contribute to training infrastructure, create tools for model evaluation and testing, and implement necessary APIs - Stay current with latest developments in foundation models and robotics, assist in literature reviews and research documentation, prepare technical reports and presentations, and contribute to research discussions and brainstorming sessions. - Work closely with senior scientists, engineers, and leaders across multiple teams, participate in knowledge sharing, support integration efforts with robotics hardware teams, and help document best practices and methodologies. About the team We leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of robotics foundation models that: - Enable unprecedented generalization across diverse tasks - Integrate multi-modal learning capabilities (visual, tactile, linguistic) - Accelerate skill acquisition through demonstration learning - Enhance robotic perception and environmental understanding - Streamline development processes through reusable capabilities
US, CA, San Francisco
Amazon is seeking an exceptional Sr. Applied Scientist to lead the development of perception systems that harness the power of radar and thermal imaging — enabling robots to perceive and operate reliably in conditions where conventional vision alone falls short. In this role, you will develop ML-driven perception pipelines for non-traditional sensing modalities, pushing the boundaries of what robots can see, understand, and act upon in challenging real-world environments. At Amazon, we leverage advanced robotics, machine learning, and artificial intelligence to solve some of the most complex operational challenges at a scale unlike anywhere else in the world. Our fleet of robots spans hundreds of facilities globally, working in sophisticated coordination to deliver on our promise of customer excellence. As a Sr. Applied Scientist in Multi-Modal Perception, you will apply deep computer vision expertise alongside classical signal processing techniques for radar and thermal imaging — modalities that provide robustness in adverse conditions and sensing capability beyond the visible spectrum. You will develop ML-based methods to extract semantic and geometric information from radar point clouds, radar tensors, and thermal imagery, and fuse these with camera and depth data to build perception systems that are reliable, comprehensive, and ready for deployment at scale. Your work will unlock new capabilities for our robots — enabling reliable detection, classification, and scene understanding in low-visibility conditions, cluttered environments, and scenarios where traditional RGB-based perception is insufficient. You will lead research that translates cutting-edge advances in deep learning and computer vision to these underexplored but high-impact sensing modalities. Join us in building the next generation of multi-modal perception systems that will define the future of autonomous robotics at scale. Key job responsibilities - Lead the research, design, and development of ML-based perception pipelines for radar and thermal/infrared imaging modalities - Develop deep learning models for object detection, classification, segmentation, and tracking using radar data (point clouds, range-Doppler maps, radar tensors) and thermal imagery - Design and implement multi-modal fusion architectures that combine radar, thermal, camera, and depth data for robust, all-condition perception - Develop novel representations and feature extraction methods tailored to the unique characteristics of radar and thermal sensors (sparsity, noise profiles, spectral properties) - Build end-to-end perception systems — from raw sensor data processing and calibration to model training, evaluation, and real-time deployment - Collaborate closely with Hardware, Navigation, Planning, and Controls teams to define sensor configurations and deliver integrated autonomy solutions - Establish benchmarks, datasets, and evaluation frameworks for radar and thermal perception - Mentor scientists and engineers; foster a culture of scientific rigor, innovation, and high-impact delivery - Publish research findings in top-tier venues (CVPR, ICCV, ECCV, ICRA, NeurIPS, etc.) and contribute to patents A day in the life - Train ML models for deployment in simulation and real-world robots, identify and document their limitations post-deployment - Drive technical discussions within your team and with key stakeholders to develop innovative solutions to address identified limitations - Actively contribute to brainstorming sessions on adjacent topics, bringing fresh perspectives that help peers grow and succeed — and in doing so, build lasting trust across the team - Mentor team members while maintaining significant hands-on contribution to technical solutions About the team Our team is a diverse group of scientists and engineers passionate about building intelligent machines. We value curiosity, rigor, and a bias for action. We believe in learning from failure and iterating quickly toward solutions that matter.