Unsupervised Learning of Discrete Action Prototypes for Effective Robot Interactions

An unsupervised algorithm is proposed to discretize a continuous robot motion space and generate "action prototypes", each producing different effects in the environment. The algorithm automatically builds a representation of the effects and groups motions into action prototypes, where motions more likely to produce an effect are represented more than those that lead to negligible changes.