Robot scheduling via learning from demonstration for tasks in medicine and more

Today’s robots are awkward co-workers because they are often unable to predict what humans need. In hospitals, robots are employed to perform simple tasks such as delivering supplies and medications, but they have to be explicitly told what to do.

Our system aims to enable robots to help assign and schedule tasks in fields ranging from medicine to the military.

Technically speaking, our new approach involves capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state-space.

We empirically demonstrate that this approach accurately learns multifaceted heuristics on both a synthetic data set incorporating jobshop scheduling and vehicle routing problems and a real-world data set consisting of demonstrations of experts solving a weapon-to-target assignment problem

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