Aiming at the application in physical human-robot interaction, this paper presents a novel adaptive admittance control scheme for robotic manipulators. Special emphasis is drawn on the avoidance of oscillatory behavior in the presence of closed kinematic chains while keeping the rendered impedance low. The approach uses an online fast Fourier transform of the measured manipulator endeffector forces in order to detect oscillations and to adapt the admittance parameters dynamically. As a novel method towards human-centered control design the adaptation strategy is determined in a user study evaluated with a machine-learning algorithm. Experiments conducted with ten human participants show superiority over the non-adaptive admittance control scheme.
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