  Haptic guidance is a powerful technique to combine the strengths of humans
and autonomous systems for teleoperation. The autonomous system can provide
haptic cues to enable the operator to perform precise movements; the operator
can interfere with the plan of the autonomous system leveraging his/her
superior cognitive capabilities. However, providing haptic cues such that the
individual strengths are not impaired is challenging because low forces provide
little guidance, whereas strong forces can hinder the operator in realizing
his/her plan. Based on variational inference, we learn a Gaussian mixture model
(GMM) over trajectories to accomplish a given task. The learned GMM is used to
construct a potential field which determines the haptic cues. The potential
field smoothly changes during teleoperation based on our updated belief over
the plans and their respective phases. Furthermore, new plans are learned
online when the operator does not follow any of the proposed plans, or after
changes in the environment. User studies confirm that our framework helps users
perform teleoperation tasks more accurately than without haptic cues and, in
some cases, faster. Moreover, we demonstrate the use of our framework to help a
subject teleoperate a 7 DoF manipulator in a pick-and-place task.
