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The study of human-robot interaction is fundamental to the design and use ofrobotics in real-world applications. Robots will need to predict and adapt tothe actions of human collaborators in order to achieve good performance andimprove safety and end-user adoption. This paper evaluates a human-robotcollaboration scheme that combines the task allocation and motion levels ofreasoning: the robotic agent uses Bayesian inference to predict the next goalof its human partner from his or her ongoing motion, and re-plans its ownactions in real time. This anticipative adaptation is desirable in manypractical scenarios, where humans are unable or unwilling to take on thecognitive overhead required to explicitly communicate their intent to therobot. A behavioral experiment indicates that the combination of goal inferenceand dynamic task planning significantly improves both objective and perceivedperformance of the human-robot team. Participants were highly sensitive to thedifferences between robot behaviors, preferring to work with a robot thatadapted to their actions over one that did not.

Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration