In this work, a novel Dynamic Movement Primitive (DMP) formulation is proposed which supports reversibility, i.e. backwards reproduction of a learned trajectory. Apart from sharing all favourable properties of the original DMP, decoupling the teaching of position and velocity profiles and bidirectional drivability along the encoded path are also supported. Original DMP have been extensively used for encoding and reproducing a desired motion pattern in several robotic applications. However, they lack reversibility, which is a useful and expedient property that can be leveraged in many scenarios. The proposed formulation is analyzed theoretically and its practical usefulness is showcased in an assembly by insertion experimental scenario.

In this work, the problem of human-robot collaborative object transfer to unknown target poses is addressed. The desired pattern of the end-effector pose trajectory to a known target pose is encoded using DMPs (Dynamic Movement Primitives). During transportation of the object to new unknown targets, a DMP-based reference model and an EKF (Extended Kalman Filter) for estimating the target pose and time duration of the human's intended motion is proposed. A stability analysis of the overall scheme is provided. Experiments using a Kuka LWR4+ robot equipped with an ATI sensor at its end-effector validate its efficacy with respect to the required human effort and compare it with an admittance control scheme.

In this work, the problem of cooperative human-robot manipulation of an object with large inertia is addressed, considering the availability of a kinematically controlled industrial robot. In particular, a variable admittance control scheme is proposed, where the damping is adjusted based on the power transmitted from the human to the robot, with the aim of minimizing the energy injected by the human while also allowing her/him to have control over the task. The proposed approach is evaluated via a human-in-the-loop setup and compared to a generic variable damping state-of-the-art method. The proposed approach is shown to achieve significant reduction of the human’s effort and minimization of unintended overshoots and oscillations, which may deteriorate the user’s feeling of control over the task.

This work proposes the utilization of sinc functions as kernels of Dynamic Movement Primitives (DMP) models for encoding point-to-point kinematic behaviors. The proposed method presents a number of advantages with respect to the state of the art, as it (i) involves a simple learning technique, (ii) provides a method to determine the minimum required number of basis functions, based on the frequency content of the demonstrated motion and (iii) provides the ability to pre-define the reproduction accuracy of the learned behavior. The ability of the proposed model to accurately reproduce the behavior is demonstrated through simulations and experiments. Comparisons with the Gaussian-based DMP model show the proposed method's superiority in terms of computational complexity of learning and accuracy for a specific number of kernels.