Sequential learning unification controller from human demonstrations for robotic compliant manipulation

Abstract Robotic compliant manipulation not only contains robot motion but also embodies interaction with the environment. Frequently endowing the compliant manipulation skills to the robot by manual programming or off-line training is complicated and time-consuming. In this paper, we propose a sequential learning framework to take both kinematic profile and variable impedance parameter profile into consideration to model a unified control strategy with “motion generation” and “compliant control”. In order to acquire this unification controller efficiently, we use a sequential learning neural network to encode robot motion and a new force-based variable impedance learning algorithm to estimate varying damping and stiffness profiles in three directions. Furthermore, the state-independent stability constraints for variable impedance control are presented. The effectiveness of the proposed learning framework is validated by a set of experiments using the 4-DoF Barrett WAM.

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