Prediction of human behaviors in the future through symbolic inference

This paper proposes an approach to construct a system which allows humanoid robots to recognize human behaviors and predict his or her future behaviors. The system consists of two modules : "motion symbol tree" and "motion symbol graph", Human demonstrator motion patterns are stored as motion symbols, which abstract the motion data by using Hidden Markov Models. The stored motion patterns are organized into a hierarchical tree structure, which represents the similarity among the motion patterns and provides abstracted motion patterns. The formed hierarchical structure is the motion symbol tree. Concatenated sequences of motion patterns are stochastically represented as transitions between the motion patterns by using an Ngram Model, and the causality among the human behaviors are extracted. This structure is the motion symbol graph. The behavioral hierarchy and transition model make it possible to predict human behaviors during observation and to generate sequences of motion patterns automatically while maintaining a natural motion stream, as if the system is a "crystal ball" to reflect future behaviors. The experiments demonstrate the validity of the proposed framework on a large scale motion data.