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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