Sensorimotor Input as a Language Generalisation Tool
Table 5: RMS Error of the Generalisation Tests
['Test', '1', '2', '3']
Using the parameter set of (50,5,70,100), the training curves (Figs. To further demonstrate the robustness of the generalisation ability given the untrained sensorimotor sequences, the validation sets, which were not included in the training, were fed into the network. In this way we aimed to test how the network responds to noun-verb combinations not used during training. Using the three MTRNNs we trained from three data-sets, we performed three generalisation experiments using the missing verb-noun combinations. In the experiments, only the first time step data in the sequence was provided (i.e. r=0 in Eq. Then the network prediction was used as the input of the next time-step and formed a closed-loop to complete 100-step of the time sequence generation. The errors of the whole three training-sets, as well as those in different steps are shown in Tab. A more straightforward visualisation of the network performance can be found in Figs. As we calculated in Tab. In particular, a larger error could be found at the beginning of each time sequence, but the network became stable and generated a stable motor trajectory with less error as time elapsed. There were some errors displayed in the trajectories generation, so sometimes the generated robot behaviours based on the trajectories are biased with the original ones.