The real test came last January, when Chernova and colleagues mocked up a real-life version of the Martian lab at the Museum of Science in Boston. Visitors were paired with a robot powered by software based on the Mars Escape data. The results were encouraging, Chernova and colleagues say in a paper to be presented next week at the International Symposium on Robot and Human Interactive Communication in Atlanta, Georgia. Sixteen out of 18 visitors worked with the robot to complete the game and most said the robot behaved rationally and contributed to their success.
Jenkins had similar success with a proof-of-principle experiment. Last year, he wired up a wheeled robot for online access and invited people to guide it through a simple maze. Over 270 people took up the challenge. He used the data they generated to build a navigation algorithm that allowed the robot to complete a maze it had not seen before.
His next experiment is more ambitious. His lab has a state-of-the-art PR2 – the same class of robot as James – that it plans to make available online. The robot will be placed in a kitchen and users will be invited to help it perform common tasks, like fetching objects from cupboards. The data they generate could help create better domestic robots, says Jenkins. The online interface will be demonstrated to researchers this August and should be available to the public by the end of the year.
The initial experiments have also flagged up some potential problems. Players in the real-life Mars Escape complained that the robot had poor communication skills, for example. This may be because the real robot often prompted different behaviour to its virtual version. For example, some visitors issued commands to move a specific distance. No players in the online game issued similar instructions, so the robot had no appropriate data to draw upon.
If such problems can be tackled, the technique has potential, says Jenkins. Many researchers focus on domestic tasks, but people in the outside world might prioritise other uses once they get control of robots. He draws an analogy with the early days of the internet: researchers built a data-sharing system and did not anticipate the emergence of Wikipedia and social networking. As for what those other uses are, Jenkins says we will have to wait: “If I had a good sense of other great applications, I would be doing them already.”
The robot uses a technology called SOINN (Self-Organising Incremental Neural Network). Osamu Hasegawa is Associate Professor at the lab and one of the system’s designers. He says in a press release: “So far, robots, including industrial robots, have been able to do specific tasks quickly and accurately. But if their environment changes slightly, robots like that can’t respond.”
This new robot can. When it is faced with the unknown, the SOINN robot uses its past experiences to make an educated guess as to what to do. It does this by “self-organising the input data it is supplied with.”
If it comes to a blank, it asks for help and can be taught how to do a new task, which it will then remember. Hasegawa adds that the system is also web-enabled and so this robot will be able to communicate with other robots to get help on how to complete a task.
The robot was filmed learning to pour a glass of water (or in this case, seeds, as water near electronics is not a good idea) but the commentator on DigInfo TV adds that the robot was next asked to produce a glass of cold water and decided itself to put down the glass and tumbler before trying to pick up the ice.
