Figure 13.

Generalization to new reaching directions. For testing the generalization ability of the proposed DMPSynergies we fix the learned shared synergies and only adapt the task-specific parameters, i.e., the mixing coefficients β_{m, k} and the time-shift parameters Δs_{m, k}. The K = 6 targets were rotated by 30 degrees, where in (A) the marker trajectories after 15, 50, 200, and 1000 episodes for a movement representation with M = 4 synergies are shown. In (B) we show the averaged learning curves for DMPSynergies with three and four synergies over 10 runs (M = 3 and M = 4). The learning curve for the unperturbed scenario from the previous experiment is denoted by the dashed line (M = 4 orig.). For a comparison the blue line denoted by DMP N = 4 illustrates the convergence rate of single task learning.