Abstract
Simple and easy human-interfaces for designing motion patterns of humanoids or human-like computer graphics have been required. One of the promising approaches is associating motion patterns with some symbol representations, and then generating new motion patterns by synthesizing those symbol representations. The proto-symbol space method based on continuous hidden Markov model (HMM) was such an approach. This method not only abstracts a symbol representation called proto-symbol but also generates new motion patterns by interpolating two proto-symbols in the proto-symbol space constructed with Kullback-Leibler divergence of the proto-symbols. Extrapolation of the proto-symbols as well as synthesis of HMMs that have different number of nodes, however, could not be achieved. In this paper, we propose an algorithm to solve these issues. We further discuss identification of synthesized proto-symbols and estimation of synthetic coefficients. To acquire accurate estimations, Bhattacharyya distance is introduced in the proto-symbol space construction. Finally, we show the feasibility of our approach with simulation experiments by a humanoid robot. This proto-symbol space could be a first synthetic step toward understanding symbolization mechanism according to Deacon's symbol developmental model.
