Assisted Teleoperation in Changing Environments with a Mixture of Virtual Guides
This addresses the problem of balancing autonomy and human control in teleoperation for robotics applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the challenge of providing effective haptic guidance in teleoperation without impairing human operator control by learning a Gaussian mixture model over trajectories using variational inference to adaptively generate haptic cues. Results show improved accuracy and sometimes faster task completion in user studies, including a demonstration with a 7 DoF manipulator.
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.