If you’ve ever helped someone move furniture, you know it takes coordination — simultaneously pushing or pulling and reacting based on what your helper is doing. Researchers have developed artificial intelligence to train robots to work together to move a couch — or in this case a long rod that served as a stand-in — around two obstacles and through a narrow door in computer simulations.
The team sought to accomplish the task with as little communication as possible between the robots. Neither robot directed the other and the two robots didn’t share their strategy in advance to complete the task. Instead, they turned to an artificial intelligence called genetic fuzzy logic. Fuzzy logic is an intelligent control technique that mimics human reasoning by replacing a simple binary classification (yes, no) with degrees of right or wrong. Genetic algorithms modify individual solutions to “learn” from past results to optimize performance over time.
Ultimately, the team wants to expand to 10 or more robots working cooperatively on a project. To build a habitat in space, many robots will need to work together. But if relying on a communications network and it goes down, the whole project fails. If robots can work independently, losing one won’t make much difference since the others can compensate to complete the mission.
The robots were given the task of carrying the virtual couch around two obstacles and through a narrow door. The robots successfully completed the task 95% of the time in simulations. More importantly, the robot work partners were 93% successful in a completely new scenario with two new unfamiliar obstacles and a target door in a different location. And the robots had nearly equal success without re-training, even when researchers changed other factors such as the size of the couch.
The control system is scalable, which means the team can add any number of robots to a task.
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