Robot Helps Nurses Schedule Tasks on Labor Floor

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Like many AI systems, the team’s robot was trained via “learning from demonstration,” which involves observing humans’ performances of tasks. But Gombolay says researchers have never been able to apply this technique to scheduling, because of the complexity of coordinating multiple actions that can be very dependent on each other.

To overcome this, the team trained its system to look at several actions that human schedulers make, and compare them to all the possible actions that are not made at each of those moments in time. From there, it developed a scheduling policy that can respond dynamically to new situations that it has not seen before.

“Rather than considering actions in isolation of each other, we crafted a model that understands why one action is better than the alternatives,” Shah says. “By considering all such comparisons, you can learn to recommend which action will be most helpful.”

The policy is “model-free,” meaning that the nurses do not have to train the robot by painstakingly ranking each possible action in each possible scenario by hand.

“You can put the robot on the labor floor, and, just by watching humans doing the different tasks, it will understand how to coordinate an efficient schedule,” says Gombolay.

The results

With this framework, the system — which the team has dubbed “apprenticeship scheduling” — can anticipate room assignments and suggest which nurses to assign to patients for C-sections and other procedures.

The approach was evaluated through experiments in which a robot provided decision-support to nurses and doctors as they made decisions on a labor floor.

Using the system on a Nao robot, nurses accepted the robot’s recommendations 90 percent of the time. The team also demonstrated that human subjects weren’t just blindly accepting advice; the robot delivered consciously bad feedback that was rejected at the same rate of 90 percent, showing that the system was trained to distinguish between good and bad recommendations.

Nurses had almost uniformly positive feedback about the robot. One said that it would “allow for a more even dispersion of workload,” while another said that it would be particularly helpful for “new nurses [who] may not understand the constraints and complexities of the role.”

“A great potential of this technology is that good solutions can be spread more quickly to many hospitals and workplaces,” says Dana Kulic, an associate professor of computer engineering at the University of Waterloo. “For example, innovative improvements can be distributed rapidly from research hospitals to regional health centres.”

Shah says that the new techniques have many uses, from turning robots into better collaborators to helping train new nurses, but the goal is not to develop robots that fully make decisions on their own.

“These initial results show there is tremendous potential for machines to collaborate with us in rich ways that will enhance many sectors of the economy,” Shah explains. “The awkward robots of the past will be replaced by valued team members.”

The RSS paper was supported by the National Science Foundation, CRICO Harvard Risk Management Foundation, and Aldebaran Robotics Inc. The IJCAI paper was supported the National Science Foundation and the U.S. Navy.

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