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