A pancake-shaped vacuum that cleans an entire room without guidance, sensing and avoiding drop-offs like stairs. An R2-D2 sized cart that navigates hospital hallways to deliver patient samples to a lab. Airline computers that man 1-800 numbers to give callers updated flight information.

Dr. Peter Stone in the Department of Computer Sciences has won four league competitions at international robot soccer challenges, and competes again this week.

Devices driven by artificial intelligence (AI) are slowly becoming part of the fabric of life. But these gadgets seem simple-minded compared to the ones Peter Stone envisions. The assistant professor of computer sciences at The University of Texas at Austin is laying the groundwork for a not-too-distant future populated by adaptable AI devices that not only can improve their interactions with objects around them, but affect each other’s actions as well.

“It’s not out of the question that in 20 years we’ll be seeing cars that drive themselves,” Stone says, noting that the cars’ abilities to communicate with each other would permit rapid, accident-free travel.

“Creating those cars is a multiagent systems problem,” he says. “It’s about learning how to develop independently controlled cars that will drive themselves by coordinating around streets, which will be a limited resource. That’s at the core of all the research problems I look at.”

His chosen path to those advances may seem unusual at first. It is paved in forest green, with the limited resource a bright orange ball. Stone and students taking his computer science course at the College of Natural Sciences are preparing a team of eight robots to compete this week at an international robot soccer competition in Padua, Italy.

An avid soccer player since childhood, Stone was on the varsity team at the University of Chicago, and plays on a top-notch amateur team in Austin. Stone became interested in using the sport to bring about advances in AI soon after beginning graduate school at Carnegie Mellon University in Pittsburgh. He saw a one-on-one robot soccer demonstration at an AI meeting in 1993 and was struck by the robots’ simplicity.

“I immediately thought, ‘Soccer is not about one robot on each team—it’s about multiple robots cooperating and working against the other team,’ ” Stone says. With his adviser’s approval, Stone began the AI project that following year.

Using miniature, Rubix cube-sized robots he helped design, he won the small-sized robot league at the first international RoboCup in 1997. He also reached the semifinals that year in the computer simulation league, in which on-screen players used an AI-based computer program of previously developed strategies to respond to patterns of soccer play.

That following year, Stone won both these international RoboCup leagues. And in 1999, he posted a shut-out during eight games in the simulator competition, winning 110-0.

“Our team became one of the ones that people started building off of, because we made a lot of our source code available,” he says.

Stone described how he created the 1999 simulation team in his book “Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer.” He also helped edit a similar book on the 2000 RoboCup competition, but notes that “the competitions are a good motivating tool, but not the be-all and end-all of it.”

Considering his research bent, it’s not surprising that the energetic scientist decided to go back to the AI drawing board after coming to Austin and the university’s Department of Computer Sciences in fall 2002. Stone’s effort focuses on Aibo (I-Bo) robots from Sony that have well-defined hardware, permitting him and his students to focus entirely on computer programming. They reworked the dog-sized robots’ code from scratch in preparation for the first American RoboCup competition, held in May, and are fine-tuning it further for the Italian competition, expected to draw tens of thousands of spectators from July 2-11.

With only 14 weeks to prepare for the warm-up competition, Stone and 19 graduate students and an undergraduate worked doggedly to improve the robots’ abilities by breaking into groups. One, led by graduate student Mohan Sridharan, focused on teaching the robots, known as the UT Austin Villa team, to distinguish the pattern of numbers on their video screens representing the pastel colors of the goals, the green of their 2.1-meter by 4.5-meter playing field, the colorful outfits of teammates and other objects. [Play video of robots in action, with Dr. Stone’s commentary. Download free QuickTime Player. For high bandwidth use: 19MB; 1 min., 58 sec. playing time]

Another group worked to improve the ‘bots’ jerky walks, and another to guide them through the steps of scoring a goal: find the ball, walk to the ball, find the goal, square up the ball with the goal, re-check that everything’s aligned and then shoot with a one-legged lunging kick or by popping front legs back together. The reverse of the latter, front power kick was taught to a “goalie” to block a ball.

In addition, the Aibos were taught to play without human input based on instructions built into their code before the games. For example, the “Dibs” program allowed an Austin Villa robot close to the ball to stake a claim to it, using an Ethernet connection to communicate that decision to teammates.

While the legged robot work was in progress, graduate student Gregory Kuhlmann and undergrads Justin Lallinger and Bharat Kejriwal applied a similar approach to improve code for the computer simulation competition. The three came in second in the American RoboCup coached computer simulation league, in which an AI-based computer program provided adaptable coaching advice to on-screen players. In the legged league, the larger crew lost in their final game of three, which was against the runner-up team from Georgia Institute of Technology.

Stone and a smaller student group have since been prepping for this week’s international RoboCup challenge. The Austin Villa ‘bots walk twice as fast as in May, thanks to programming tweaks provided by graduate student Daniel Stronger, and can scan the field and kick faster as well thanks to graduate student Peggy Fidelman and undergraduate Ellie Lin. They’re also able to tuck a ball under the chin as cargo, or do a head-butt to send it flying sideways.

“We’re now ready in this last stretch before Italy to focus on completely new behaviors that can take advantage of these better building blocks,” Stone says.

Mohan Sridharan, graduate student in Electrical and Computer Engineering, works to refine the robots’ visual acuity.

For example, the robots are being programmed to detect where they and their teammates are on the playing field so that programs like “Dibs” are easier to carry out. These programs, which create what Stone refers to as “locker room” agreements that are made before competition, will be a key part of the computer simulation teams in Italy, and play a role in Stone’s other AI-based activities.

An example is the mock travel agent competitions that he has been a part of since 2000. The competitions involve on-screen agents haggling to create the best vacation packages based on various flight, hotel and entertainment options. Stone’s team won the 2000 Trading Agent Competition and was one of two declared winners the following year. He hopes his team fares as well in this year’s competition in August.

Though Stone always considers these more practical applications of autonomous machinery, he admits to feeling a rush when considering RoboCup creator Hirokai Kitano’s ultimate vision: creating a team of autonomous humanoid robots by 2050 that duke it out and win against a real soccer team.

“That’s akin to the dream of landing a man on the moon,” Stone says, noting that this dream will keep AI researchers challenged for decades.

Other AI dreams in their infancy include robots that can perform search and rescue operations for military or other purposes, and a network of computers whose nodes can sense outages and autonomously decide the quickest ways to re-route e-mail.

As the dreams become reality, Stone notes that people will tend to forget the machine learning behind them. Consider, for example, the case of shoppers who think nothing about AI when they go to a Web site and receive suggestions of other products to purchase, or chess-playing programs that make moves without input. There is an advantage to these fluid expectations, though.

“It means we have the freedom to keep looking 20 years into the future,” Stone says.

And you can bet that he’ll travel the journey of a thousand innovations needed to get there a single, well-planned robot step at a time.

Barbra Rodriguez

Photos: Marsha Miller