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 have developed an algorithmic planner that helps delegate tasks to humans and robots.
The planner is called "Act, Delegate or Learn" (or ADL). It considers a list of tasks and decides how best to assign them. To develop ADL, researchers asked three questions:
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. Given all those costs, ADL gives you the optimal division of labor. ADL can be useful in manufacturing and assembly plants, for sorting packages, or in any environment where humans and robots collaborate to complete several tasks.
- When should a robot act to complete a task?
- When should a task be delegated to a human?
- When should a robot learn a new task?
The researchers tested ADL in scenarios where humans and robots had to insert blocks into a peg board and stack parts of different shapes and sizes made of Lego bricks. 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 software is among the first to include robot learning as part of its reasoning. This is important because Robots aren’t static anymore. They can be "improved," and they can be taught.
Often in manufacturing, a person will manually manipulate a robotic arm to teach the robot how to complete a task. Teaching a robot takes time and, therefore, has a high upfront cost. But it can be beneficial in the long run if the robot can learn a new skill. Part of the complexity is deciding when it is best to teach a robot versus delegating the task to a human. This requires the robot to predict what other tasks it can complete after learning a new task.
Given this information, ADL converts the problem into a mixed integer program - an optimization program commonly used in scheduling, production planning or designing communication networks - that can be solved efficiently by off-the-shelf software. ADL performed better than traditional models in all instances and decreased the cost of completing the tasks by 10% to 15%.