Once a human-machine team has formed a high-quality, flexible plan for working together, the robot must execute its part of the plan and work with its teammate.

The challenge is that in real life things do not often go exactly as planned. This project levels algorithms that leverage models predictive models of humans and flexible plans to work better in teams. New algorithms are developed to adjust task allocations and the timing of a robot’s actions to the temporal preferences, or “rhythm,” of a person’s actions. Experiments involving live interactions with people demonstrate that these new algorithms significantly enhance the ability of people and robots to work flexibly together.

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