A human inspired handover policy using Gaussian Mixture Models and haptic cues

A handover strategy is proposed that aims at natural and fluent robot to human object handovers. For the approaching phase, a globally asymptotically stable dynamical system (DS) is utilized, trained from human demonstrations and exploiting the existence of mirroring in the human wrist motion. The DS operates in the robot task space thus achieving independence with respect to the robot platform, encapsulating the position and orientation of the human wrist within a single DS. It is proven that the motion generated by such a DS, having as target the current wrist pose of the receiver’s hand, is bounded and converges to the previously unknown handover location. Haptic cues based on load estimates at the robot giver ensure full object load transfer before grip release. The proposed strategy is validated with simulations and experiments in real settings.

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