In this paper we propose a new approach for efficient programming of grinding and polishing operation. In the proposed system, the initial policy is performed by a skilled operator and recorded with a passive digitizer. The demonstrated policy comprises both position and force data. The optimal robot execution of the task is provided by applying a virtual mechanism approach, which models the polishing/grinding tool as a serial kinematic chain. By joining the robot and the virtual mechanism in an augmented system, additional degrees of freedom are obtained and redundancy resolution can be applied to optimize the demonstrated motion. Another benefit of the proposed approach is that the same policy can be transferred to different combination of robots and grinding/polishing tools without any modification of the captured motion. The proposed approach requires known contact point between the treated object and the polishing/grinding tool. We propose a novel approach for accurate estimation of this point using data obtained from the force-torque sensor. Finally, the demonstrated path is refined to compensate for inaccurate calibration and different dynamics of a robot and the human demonstrator using iterative learning controller. The proposed method was verified in a real industrial environment.

A teleexistence manipulation system was evaluated quantitatively by comparing tasks of tracking a randomly moving target under several operational conditions. The effects of various characteristics, e.g., binocular vision and the effect of natural arrangement of the head and the arm, are analyzed by comparing quantitatively the results under these operational conditions. A human tracking transfer function was measured and used for comparison. The results revealed the significant dominance of the binocular vision with natural arrangement of the head and arm, which is the most important characteristic of teleexistence.

We propose a new approach to programming by the demonstration of finishing operations. Such operations can be carried out by industrial robots in multiple ways because an industrial robot is typically functionally redundant with respect to a finishing task. In the proposed system, a human expert demonstrates a finishing operation, and the demonstrated motion is recorded in the Cartesian space. The robot's kinematic model is augmented with a virtual mechanism, which is defined according to the applied finishing tool. This way, the kinematic model is expanded with additional degrees of freedom that can be exploited to compute the optimal joint space motion of the robot without altering the essential aspects of the Cartesian space task execution as demonstrated by the human expert. Finishing operations, such as polishing and grinding, occur in contact with the treated workpiece. Since information about the contact point position is needed to control the robot during the operation, we have developed a novel approach for accurate estimation of contact points using the measured forces and torques. Finally, we applied iterative learning control to refine the demonstrated operations and compensate for inaccurate calibration and different dynamics of the robot and human demonstrator. The proposed method was verified on real robots and real polishing and grinding tasks.Note to Practitioners-This work was motivated by the need for automation of finishing operations, such as polishing and grinding, on contemporary industrial robots. Existing approaches are both too complex and too time-consuming to be applied in flexible and small-scale production, which often requires the frequent deployment of new applications. Our approach is based on programming by demonstration and enables the programming Manuscript

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