Key points are not available for this paper at this time.
This paper introduces a Personalized Adaptive Stiffness controller for physical Human-Robot Interaction and validates its performance in an extensive user study with 49 participants. The controller is calibrated to the user's force profile to account for inter-user variance and individual differences. The user study compares the new scheme to conventional fixed stiffness or gravitation compensation controllers on the 7-DOF KUKA LWR IVb by employing two typical joint-manipulation tasks. Somewhat surprisingly, the experiments suggest that for simpler tasks a standard fixed controller may perform sufficiently well and that respective task dependency strongly prevails over individual differences. In the more complex task, quantitative and qualitative results clearly show differences between the different control modes and a both a performance gains and a user preference for the Personalized Adaptive Stiffness controller.
Gopinathan et al. (Tue,) studied this question.