Skill learning from observations

This paper presents a new approach for representing and registering skills based on state transition graphs. First, we generate clusters of feasible state transitions from the experimental data such that the union of these clusters represents the feasible state transition region of the system. Globally competitive and locally cooperative (GCLC) algorithm is formulated for self-organizing clusters and state transitions accurately through interpolation. A state transition graph is then constructed by representing possible transitions between clusters. Skills are considered embedded in this state transition graph and can be extracted by searching for an optimal path from the current to the goal states. The reactive nature of skills can be implemented by forming priority indices for sibling branches for local decision with possible merging and splitting of clusters to ensure consistency in prioritization. Experimental results are shown.<>