In this paper, we propose a novel P-TPs framework, which is both frequency and phase-dependent. Thereby, the executed P-CMPs can be easily modulated, and consequently, the learning rate can be improved. Moreover, both the kinematic and the dynamic profiles are parameterized, thus enabling the representation of skills using corresponding parameters. The proposed framework was evaluated on two robot systems, i.e., Kuka LWR-4 and Franka Emika Panda. The evaluation of the proposed approach on a Kuka LWR-4 robot performing a swinging motion and on Franka Emika Panda performing an exercise for elbow rehabilitation shows fast P-CTPs acquisition and accurate and compliant motion in real-world scenarios.
COBISS.SI-ID: 36944899
This paper presents an optimization approach for shaping the robot EE Cartesian stiffness. This research targets collaborative robots with intrinsic compliance – serial elastic actuators (SEAs). Although robots with SEAs have constant joint stiffness, task redundancy (null-space) for a specific task could be used for robot reconfiguration and shaping the stiffness matrix while still keeping the EE position unchanged. The method proposed in this paper to investigate null-space reconfiguration's influence on Cartesian robot stiffness is based on the Sequential Least Squares Programming (SLSQP) algorithm, which presents an expansion of the quadratic programming algorithm for nonlinear functions with constraints.
COBISS.SI-ID: 60709635
In this paper we examined physical collaboration between two individuals using a dual-arm robot as a haptic interface. Each subject performed the task individually and linked through the guided robot arms with a virtual model to perform the same task in collaboration. We evaluated both individual and collaborative performances based on Fitts’ law, which describes the relation between the speed of motion and its accuracy. The results show that the Fitts’ law applies both on individual and collaborative tasks, with their performance improving when in collaboration.
COBISS.SI-ID: 20340483