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Want a hybrid workforce? The trick is getting humans and machines speaking to each other

ZDNet

A stealth company is trying to solve one of the oddest interoperability problems of the modern era: How do you get robots and non-engineers talking to each other? Founded by the former Director of Robotics for Google, the company, Formant, is making its first public bow comes thanks to a recently-announced $6 million in funding from SignalFire. Formant's pitch is straightforward, and it illustrates the peculiar problem of automation in 2019: Robots perform a lot of tasks in industries like logistics and manufacturing, but those industries still rely on humans for crucial decisions robots can't yet make. Getting robots and humans communicating in real time to facilitate that decision-making has been tricky and usually requires an intermediary in the form of an engineer. Global demand for robots is expected to exceed $77 billion by 2022, nearly doubling since 2017.


I 'walked' Boston Dynamics' robot dog around San Francisco

Mashable

"Can I take a photo?" The Boston Dynamics robot dog known as Spot stood out. Others were annoyed and wary. Most, however, seemed excited to see the four-legged robot in person. That's what I noticed from more than 4,500 miles away from home in Lima, Peru, while remotely operating the quadruped as it scurried along a busy sidewalk in San Francisco's North Beach neighborhood.


Serve Food in Far-Away Restaurants--Right From Your Couch

WIRED

David Tejeda helps deliver food and drinks to tables at a small restaurant in Dallas. Sometimes he lends a hand at a restaurant in Los Angeles too. Tejeda does all this from his home in Belmont, California, by tracking the movements and vital signs of robots that roam around each establishment, bringing dishes from kitchen to table, and carrying back dirty dishes. Sometimes he needs to help a lost robot reorient itself. "Sometimes it's human error, someone moving the robot or something," Tejeda says.


Who are the Visionary companies in robotics? See the 2020 SVR Industry Award winners

Robohub

These Visionary companies have a big idea and are well on their way to achieving it, although it isn't always an easy road for any really innovative technology. In the case of Cruise, that meant testing self driving vehicles on the streets of San Francisco, one of the hardest driving environments in the world. Some of our Visionary Awards go to companies who are opening up new market applications for robotics, such as Built Robotics in construction, Dishcraft in food services, Embark in self-driving trucks, Iron Ox in urban agriculture and Zipline in drone delivery. Some are building tools or platforms that the entire robotics industry can benefit from, such as Agility Robotics, Covariant, Formant, RobustAI and Zoox. The companies in our Good Robot Awards also show that'technologies built for us, have to be built by us'.


Ultra2Speech -- A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images

arXiv.org Machine Learning

Thousands of individuals need surgical removal of their larynx due to critical diseases every year and therefore, require an alternative form of communication to articulate speech sounds after the loss of their voice box. This work addresses the articulatory-to-acoustic mapping problem based on ultrasound (US) tongue images for the development of a silent-speech interface (SSI) that can provide them with an assistance in their daily interactions. Our approach targets automatically extracting tongue movement information by selecting an optimal feature set from US images and mapping these features to the acoustic space. We use a novel deep learning architecture to map US tongue images from the US probe placed beneath a subject's chin to formants that we call, Ultrasound2Formant (U2F) Net. It uses hybrid spatio-temporal 3D convolutions followed by feature shuffling, for the estimation and tracking of vowel formants from US images. The formant values are then utilized to synthesize continuous time-varying vowel trajectories, via Klatt Synthesizer. Our best model achieves R-squared (R^2) measure of 99.96% for the regression task. Our network lays the foundation for an SSI as it successfully tracks the tongue contour automatically as an internal representation without any explicit annotation.