Robots Teach Themselves to Collaborate More Effectively

Researchers from the University of Massachusetts Amherst have demonstrated that programming robots to create their own teams and to voluntarily wait and work with their teammates results in faster task completion.

In an industrial setting, a team of robots can be less expensive to deploy but efficiently coordinating their diverse abilities can be difficult.

To address this challenge, the researchers created a learning-based approach for scheduling robots, called learning for voluntary waiting and subteaming (LVWS).

The method taught the robots to voluntarily wait for the other robots when optimal, for example, to complete a bigger task together rather than immediately performing a smaller task.

The LVWS approach was tested with six robots given 18 tasks in a computer simulation. The results were compared with four other methods. These were then benchmarked against a known, perfect solution for completing the scenario in the fastest amount of time to see how suboptimal they performed in comparison.

The new method was 0.8% suboptimal, compared with 11.8% to 23% for the other methods, meaning it was close to the best possible theoretical solution.

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