Construction of a space of motion labels from their mapping to full-body motion symbols

Language is an indispensable for humanoid robot to be integrated into daily life. This paper proposes a novel approach to construct a space of motion labels from their mapping to human whole body motions. The motions are abstracted by Hidden Markov Models, which are referred to as motion symbols. The human motions are automatically partitioned into motion segments, and recognized as sequences of the motion symbols. Sequences of motion labels are also assigned to these motions. The referential relationship between the motion symbols and the motion labels is extracted by stochastic translation model, and distances among the labels are calculated from the association probability of the motion symbols being generated by the labels. The labels are located in a multidimensional space so that the distances are satisfied, and it results in a label space. The label space encapsulates relations among the motion labels such as their similarities. The label space also allows motion recognition. The validity of the constructed label space is demonstrated on a motion capture data-set.