firsthand candidate campuses. One evening in Pittsburgh, the pair of them threw pebbles up at Whittaker’s window to convince the robotics legend to give the teenager a tour of the Field Robotics Center. That experience was the reason Montemerlo attended CMU. Years later, Whittaker would become Montemerlo’s PhD adviser; in the same period, Montemerlo also happened to be Chris Urmson’s officemate.
At Stanford, Montemerlo’s presentation amounted to a travelogue of his experiences at the California Speedway. Full of photos of the various robots, the seminar highlighted the problems and foibles that each team experienced. He spent a lot of time on the work that had almost been destroyed by Sandstorm’s rollover accident. The penultimate slide asked whether the Stanford AI lab should compete in the second DARPA Grand Challenge. The final slide featured the answer: “No,” in bold and all caps.
Thrun is a slim man who communicates in perfectly enunciated, precisely formed sentences colored with a German accent; he was born in the small Rhineland city of Solingen and raised in north Germany. “Why not?” he asked softly.
“It’s hard,” said Montemerlo, whose side-parted brown hair and wire-framed circular glasses made him resemble the Hollywood stereotype of a software engineer. “It’s all encompassing,” he followed up, perhaps thinking of the experience of Urmson and the rest of the CMU team. “People have to work all day and all night. They lose their social life. And—it can’t be done!”
Somewhere, somehow, Montemerlo must have known that telling Thrun that something couldn’t be done was the quickest way to entice him to try it. “I’m a rule breaker,” Thrun says, a character trait he shares with Whittaker. “A rebel—I like to do crazy things.”
Thrun was the third of three children. “I was the one the parents didn’t have the energy and time to pay attention to,” he told one reporter, years later. “I remember a beautiful childhood—but pretty much on my own.” Left to his own devices, he developed various obsessions with intellectual projects. At the age of twelve, in 1980, the obsession involved a Texas Instruments pocket calculator that could be programmed to solve various equations. Thrun delighted himself using it to create little video games. Next, he happened upon a Commodore 64 personal computer on display in a local department store. The computer was too expensive for his middle-class family, so Thrun returned to the store display to program on it, day after day, week after week. Each day he tried bigger and bigger programming challenges. He grew adept at efficient coding; because the staff turned off the computer each night, he had to execute each challenge he set himself in the two and a half hours that passed between the end of the school day and the store’s closing time.
By the time Thrun’s parents bought him a used NorthStar Horizon personal computer, the young man was able to program simple video games. He wrote a virtual simulation of the Rubik’s Cube. Another feat involved coding the member database for his family’s tennis club. One gets a sense that Thrun roved through his adolescence seeking out challenging problems that he would use to test his programming ability. The same method would predominate in Thrun’s academic and professional life. He enrolled in the computer science department at the University of Bonn. Artificial intelligence attracted him because, in comparison to humans, with their sometimes irrational, inscrutable behavior, Thrun felt he could fully grasp the reasons a software program acted the way it did.
In 1990, the University of Bonn bought a Japanese robotic arm as a research tool. Thrun distinguished himself by using a neural network to teach the robot how to catch a rolling ball. The resultant academic paper was accepted to an American artificial intelligence conference, Neural Information Processing Systems. The trip was a turning point for Thrun, who was then twenty-two. He’d discovered people exactly like him—a whole community of “psychologists and statisticians and computer scientists all working together to understand how to make machines learn.” From that moment on Thrun focused on writing academic papers so he could attend more AI conferences. Through such gatherings, Carnegie Mellon AI legend Alex Waibel became a mentor, as did Thrun’s future thesis adviser, Tom Mitchell. Thrun joined the CMU faculty after he earned his PhD in computer science and statistics from the University of Bonn in 1995.
One of the most interesting projects Thrun worked on in Pittsburgh was the creation of a robot tour guide for museums. In keeping with the kitsch factor the public associated with robots—think the 1986 comedy Short Circuit, the TV show Knight Rider and Data, the well-meaning android on Star Trek: The Next Generation—the tour guide that Thrun constructed, Minerva, included a pair of camera lenses for eyes and a red mouth that could tilt into a frown to indicate displeasure. As a publicity stunt to demonstrate the capabilities of technology, Minerva even provided tours to visitors of Washington’s Smithsonian Museum.
It turned out programming a robot to navigate through a museum was a surprisingly complex challenge. Minerva would share the museum floor with dozens of human tourists—as well as valuable museum exhibits. How to engineer the creature so that it didn’t bump into an exhibit? How to write the code so that it didn’t roll over a child?
Six years before DARPA staged its first Grand Challenge, in 1998, Thrun equipped Minerva with laser-range finders. Then he loaded the robot with a machine-learning algorithm and sent it out on the museum floor at night, without any tourists around. Minerva wandered around the exhibits, sending out laser beams and creating a map of its environment. Then, when the museum was open, with humans sharing the same floor as the robot, Minerva would use this map to locate itself. The map also provided a way for Minerva to avoid running into humans. The robot would assume any new obstacle that hadn’t been on the original map was a human, causing Minerva to stop safely.
The tour guide was a big hit, and Thrun used the acclaim to handle the software side of other projects. For example, Whittaker convinced Thrun to join the team that built the Groundhog robot that aimed to make it safer for Appalachian coal miners to retrieve their underground ore. Maps didn’t exist for older, decommissioned mines in the area, which could cause problems. In 2002, for example, nine workers toiling in Pennsylvania’s Quecreek mine were trapped by water when they breached an adjacent passageway that had been abandoned for years and flooded sometime along the way. The miners escaped after three days, but Whittaker took the accident as a challenge and, in just two months, with Thrun working on the SLAM programming, created a robot that could be dropped into old mines to scan the passageways and create 3-D maps for reference.
DARPA’s series of challenges fascinated Thrun. When Thrun was eighteen, in 1986, his best friend, Harald, was invited for a ride in another friend’s new Audi Quattro. It was an icy day, and the driver was going too fast and ran the Quattro headfirst into a truck. Harald died instantly. The impact was so strong that his seat belt was shredded. The crash would forever haunt the German robotics professor.
Thrun saw self-driving cars as a way to make automobile transportation safer, to avoid crashes like the one that killed his friend. He did some thinking about the problem after the first Grand Challenge. The fact that DARPA created waypoints along the route really simplified the problem, he figured. Programming Minerva to navigate the fast-changing and crowded environment of the Smithsonian Museum rivaled the complexity of the self-driving-car problem. Before he left Carnegie Mellon, he went to Red Whittaker with an offer. “Look,” Thrun told the older robotics legend. “I’ve been recruited from Stanford, but for the next Grand Challenge, I would love to help you.”
“Had he said yes,” Thrun recalls, “I would have happily served on his team and never have started my own team.”
But Whittaker declined Thrun’s offer, presumably because he wanted to keep Red Team exclusive to people associated with Carnegie Mellon. After Montemerlo’s presentation, Thrun considered whether to enter the second challenge himself. Red Team had taken a year to build a robot that went 7.3 miles. If Thrun’s new lab could do better, they’d go a long way toward establishing a national reputation. SLAM would be integral to a successful performance, and Thrun and Montemerlo were
