The paper presents a two-layered system for (1) learning and encoding a periodic signal without any knowledge on its frequency and waveform, and (2) modulating the learned periodic trajectory in response to external events. The system is used to learn periodic tasks on a humanoid HOAP-2 robot. The first layer of the system is a dynamical system responsible for extracting the fundamental frequency of the input signal, based on adaptive frequency oscillators. The second layer is a dynamical system responsible for learning of the waveform based on a built-in learning algorithm. By combining the two dynamical systems into one system we can rapidly teach new trajectories to robots without any knowledge of the frequency of the demonstration signal. The system extracts and learns only one period of the demonstration signal. Furthermore, the trajectories are robust to perturbations and can be modulated to cope with a dynamic environment. The system is computationally inexpensive, works on-line for any periodic signal, requires no additional signal processing to determine the frequency of the input signal and can be applied in parallel to multiple dimensions. Additionally, it can adapt to changes in A. Gams ( ) · J. Lenarčič "Jožef Stefan" Institute, frequency and shape, e.g. to non-stationary signals, such as hand-generated signals and human demonstrations.

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Viscoelastic properties of muscles and tendons have an important influence on human motion performance. Proper determination of these properties is essential in the analysis and modelling of human motion dynamics. The purpose of our study was to develop a method for in vivo determination of the viscoelastic properties of the entire triceps surae muscle-tendon complex (MTC) including the gastrocnemius. Ten trained male subjects participated in this study. The measurement procedure consisted of two parts: soleus and Achilles tendon stiffness and viscosity were determined in the first part while the gastrocnemius stiffness and viscosity were determined in the second part. The measurement device and the procedure have been designed in such a manner that as few human body segments move as possible during the measurement. Thus, the measurement uncertainty due to the approximation of the properties of the human body segments was minimized. Triceps surae MTC viscoelastic properties of both legs were measured for each subject. There were no significant differences in viscoelastic coefficients for left and right lower extremities; however, there were noticeable differences between subjects. The soleus stiffness coefficient was greater than the gastrocnemius stiffness coefficient by 87.6 m(-1) in average. For all subjects, soleus viscosity was equal or greater than gastrocnemius viscosity. Values of viscoelastic parameters obtained by our method can be used in the analysis and modelling of human movement in situations where the knee joint is not necessarily flexed and there is coactivation of the soleus and the gastrocnemius.

This paper investigates the extent to which biarticular actuation mechanisms—spring-driven redundant actuation schemes that extend over two joints, similar in function to biarticular muscles found in legged animals—improve the performance of jumping and other fast explosive robot movements. Robust numerical optimization algorithms that take into account the complex dynamics of both the redundantly actuated system and frictional contact forces are developed. We then quantitatively evaluate the gains in vertical jumping vis-à-vis monoarticular and biarticular joint actuation schemes and examine the effects of spring stiffness and activation angle on overall jump performance. Both numerical simulations and experiments with a hardware prototype of a biarticular legged robot are reported.