What robots in space teach us about teamwork: A deep dive into NASA
Note from the Editor, Tricia Wang: The final contributor in the Co-designing with machines edition is Janet Vertesi, (@cyberlyra), assistant professor of sociology at Princeton University, urging us to think about organizations when we talk about robots. To overcome the human-machine binary, she takes us into her years of fieldwork with NASA’s robotic teams to show us that robotic work is always teamwork, never a one-to-one interaction with robots. It’s not easy to get inside organizations, much less a complicated a set of institutions such as NASA, but that is why Janet’s writings are a rare but powerful examination of how robots are actually used. She is a frequent op-ed contributor to places like CNN. Following her first book on NASA’s Mars Rover Expedition, she is already working on her second book about robots and organizations.
One robot, many humans
I study NASA’s robotic spacecraft teams: people for whom work with robots is not some scifi fantasy but a daily task. Their robotic teammates roll on planetary surfaces or whip past the atmospheres of gas giants and icy moons at tremendous speeds.
It is often easy to forget about these earth-bound groups behind the scenes when we are transfixed by new images of distant worlds or the achievements of these intrepid machines. We might only catch a quick glimpse of a few people in a room, an American flag on the wall behind them, cheering when a probe aces a landing or swings into orbit: like this week, when Juno arrived at Jupiter. But this is only a small fraction of the team. Not only are the probes complex and require a group of engineers to operate and maintain them safely, but the scientific requirements for each mission bring together many diverse experts to explore new worlds.
Robotic work is team work
To that end, working with a spacecraft is always teamwork, a creative task that brings together hundreds of people. Like any team, they use local norms of communication and interaction, and organizational routines and culture, in order to solve problems and achieve their goals. The spacecraft exploring our solar system have enough artificial intelligence to know better than to drive off a cliff, or they may know to reset their operating systems in case of a fault. There the autonomy ends. For the rest, every minute down to the second of their day is part of a plan, commanded and set into code by specialists on earth.
How to decide what the robot should do? First the team must take into account some basic constraints. When I studied the Mars Exploration Rover mission team, everyone knew that Opportunity could not drive very quickly; lately it has suffered from memory lapses and stiff joints in its old age. On another mission I have studied as an ethnographer, the path the spacecraft takes is decided years in advance to take into account the planetary system’s delicate orbital dynamics and enable the team to see as much of the planet, its moons and rings as possible. It is not easy to change course. On all missions, limits of power, time, and memory on board matter provide hard constraints for planning.
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Note from the Editor, 
Note from the Editor, 
Recently we have seen how Artificial Intelligence and Machine Learning can amaze us with seemingly impossible results like AlphaGo. We also see how machines can generate fear with perceived “machine-like” reasoning, logic and coldness, generating potentially destructive outcomes with a lack of humanity in decision making. An example of the latter that has become popular is how self driving cars decide to choose between two bad outcomes. In these scenarios, the AI and ML are embodied as a machine of some sort, either physical like a robot or car, or a “brain” like a predictive crime algorithm made popular in the book and film “Minority Report” and more recently TV show “Persons of Interest.
But let me step back for a minute. Machines can not do those things above in a general sense. For example, if I put my laptop in a gym with a basketball, it can’t evolve a body and learn to play basketball. That is, it can’t currently do that without the help of many bright engineers and scientists. If I downloaded all my health data into my phone, my phone is not going to learn how to treat my health issues and notify my doctor. Again, that is it can’t do that currently without the help of many smart engineers and scientists. So while my machine can’t become human today on its own, with the help of many engineers and scientists solving some very interesting technology, user experience, and domain specific problems, machines can do some very remarkable things, like drive a car or engage in conversation.
Note from the Editor, 
Letter from the Editor: I am happy to announce the 
As computers become more human, we need to work even harder to resist the binary of computers versus humans. We have to recognize that humans and machines have always interacted as a symbiotic system. Since the dawn of our species, we’ve changed tools as much as 
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