Algorithm To Divvy Up Tasks for Human-Robot Teams

As robots increasingly join people on the factory floor, in warehouses and elsewhere on the job, dividing up who will do which tasks grows in complexity and importance. People are better suited for some tasks, robots for others. And in some cases, it is advantageous to spend time teaching a robot to do a task now and reap the benefits 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, named ADL, short for "Act, Delegate or Learn," considers a list of tasks 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. candidate in the RI.