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Algorithm Optimally Divvies up Tasks for Human-Robot Teams

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Using algorithms and software to decide how to delegate and divide labor is not new, even when robots are part of the team. However, this work is among the first to include robot learning in its reasoning.

Credit: SciTechDaily

As robots increasingly join people working on the factory floor, in warehouses, and elsewhere on the job, determining who will do which tasks increases in complexity and importance. People are better suited for some jobs, robots for others. And in some cases, it is advantageous to spend time teaching a robot to do a task now and reap the rewards later.

Researchers at Carnegie Mellon University's Robotics Institute (RI) have developed an algorithmic planner that helps delegate tasks to humans and robots. The planner, "Act, Delegate or Learn" (ADL), considers a list of duties and decides how best to assign them. The researchers asked three questions: When should a robot act to complete a task? When should a task be delegated to a human? And when should a robot learn a new task?

“There are costs associated with the decisions made, such as the time it takes a human to complete a task or teach a robot to complete a task and the cost of a robot failing at a task,” said Shivam Vats, the lead researcher and a Ph.D. student in the RI. “Given all those costs, our system will give you the optimal division of labor.”

From SciTechDaily
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