"One of the dreams I had as a kid was about the first day of school, and being able to build and be creative, and it was the happiest day of my life. And at MIT, I felt like that dream became reality," says Ballesteros. Growing up in the suburban town of Spring, Texas, just outside of Houston, Erik Ballesteros couldn't help but be drawn in by the possibilities for humans in space. It was the early 2000s, and NASA's space shuttle program was the main transport for astronauts to the International Space Station (ISS). Ballesteros' hometown was less than an hour from Johnson Space Center (JSC), where NASA's mission control center and astronaut training facility are based.

It's hard not to laugh at NASA's blooper reel of astronauts falling and bouncing in slow motion on the moon. But coping with inertia where gravity is one-sixth that of Earth is no laughing matter when you're wearing a constricting space suit and need to finish an exhausting task. So mechanical engineering professor Harry Asada (center) and colleagues are developing wearable robotic limbs to help astronauts get back on their feet after a fall. Based on the "SuperLimbs" Asada designed to assist construction workers and ship builders, the limbs extend from a backpack that would also contain the astronaut's life support system along with a controller and motors to provide power. As part of NASA's planned Artemis mission, astronauts will be expected to build the first permanent moon base--a physically demanding project with a high risk of falls during multiple extended extravehicular activities (EVAs).

This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more challenging when they intend to operate tools while keeping control of body postures in 3D with the various diving suits and gears. The current literature is limited in underwater intention recognition, impeding the development of intelligent wearable systems for human-robot interactions underwater. Here, we present a novel solution to simultaneously detect head motion and throat vibrations under the water in a compact, wearable design. Experiment results show that using machine learning algorithms, we achieved high performance in integrating these two modalities to translate human intentions to robot control commands for an underwater superlimb system. This study's results paved the way for future development in underwater intention recognition and underwater human-robot interactions with supernumerary support.