Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems. In order to improve the sampling efficiency and decrease the required interactions during the learning process, this paper develops a data-efficient learning variable impedance control method that enables the industrial robots automatically learn to control the contact force in the unstructured environment. To this end, a Gaussian process model is learned as a faithful proxy of the system, which is then used to predict long-term state evolution for internal simulation, allowing for efficient strategy updates. The effects of model bias are reduced effectively by incorporating model uncertainty into long-term planning. Then the impedance profiles are regulated online according to the learned humanlike impedance strategy. In this way, the flexibility and adaptivity of the system could be enhanced. Both simulated and experimental tests have been performed on an industrial manipulator to verify the performance of the proposed method.

Kinematic parameters' calibration is a powerful method to improve the accuracy of the robot. This paper proposes an effective kinematic self-calibration method for dual-manipulators based on virtual constraints to estimate the actual kinematic parameters of the robots. This method only needs a camera mounted on one robot end-effector (EE) and a calibration target attached to another robot EE. First, a new calibration error model based on the straight line constraint is established to formulate the positions' misalignment error with the kinematic parameters' error. Then, the particle swarm optimization algorithm is developed to generate the optimal calibration poses of the robots under the constraints, which are used to ensure the poses feasible and the measurement errors acceptable. Finally, the kinematic parameter errors are identified with the Levenberg-Marquardt algorithm. The experiments of the kinematic parameters' calibration with the dual-manipulators system are designed. The experimental results showed that the high positional accuracy of both robots can be achieved.

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