Figure 4.

Results for the dynamic via-point task. The goal of this simple multi-task learning problem is to pass through five via-points, denoted by the large dots in (A) and navigate to the target state at 1. The corresponding controls (accelerations) of this dynamical system are shown in (B). These five trajectories are simultaneously learned using DMPSynergies with a single synergy (M = 1) represented by N = 2 Gaussians. We compare to dynamic movement primitives (DMPs) with N = 8 Gaussians and to an incremental variant of DMPs in (C). For the DMP approaches each task (via-point) has to be learned separately. Thus, the two learning curves have five peaks. In contrast with DMPSynergies we could learn these five tasks at once, which resulted in faster overall convergence. The plot in (D) illustrates the mean and the standard deviation of the learned β values for the DMPSynergy approach. Via interpolating β and by reusing the learned synergy new motor skills can be generated without re-learning. This is illustrated in (E), where β ∈ [0.07, 0.34].