Calibration is necessary for dual manipulator to complete operational tasks. This paper proposes an effective robot-robot and hand-eye calibration method based on virtual constraints. Firstly, a rotational error model and a translational error model are established based on the relationships between the transformation matrices of the dual manipulator calibration system. Then a poses-alignment method is designed to make the poses of the two robots satisfy the constructed virtual constraints. At the aligned positions, the joint angles of the two robots are saved and used to calculate the values of the variables in the error models. Finally, the robot-robot and hand-eye rotational errors are estimated by an iterative algorithm. These errors are then used to calculate translational errors based on the SVD (singular value decomposition) method. To show the feasibility and effectiveness of the proposed method, experiments of robot-robot and hand-eye calibration for dual manipulators are performed. The experiment results demonstrate that the accuracy of the dual manipulator system is improved greatly.

Compared with the robots, humans can learn to perform various contact tasks in unstructured environments by modulating arm impedance characteristics. In this article, we consider endowing this compliant ability to the industrial robots to effectively learn to perform repetitive force-sensitive tasks. Current learning impedance control methods usually suffer from inefficiency. This paper establishes an efficient variable impedance control method. To improve the learning efficiency, we employ the probabilistic Gaussian process model as the transition dynamics of the system for internal simulation, permitting long-term inference and planning in a Bayesian manner. Then, the optimal impedance regulation strategy is searched using a model-based reinforcement learning algorithm. The effectiveness and efficiency of the proposed method are verified through force control tasks using a 6-DoFs Reinovo industrial manipulator.