Probabilistic semantic models for manipulation action representation and extraction

In this paper we present a hierarchical framework for representation of manipulation actions and its applicability to the problem of top down action extraction from observation. The framework consists of novel probabilistic semantic models, which encode contact relations as probability distributions over the action phase. The models are action descriptive and can be used to provide probabilistic similarity scores for newly observed action sequences. The lower level of the representation consists of parametric hidden Markov models, which encode trajectory information. © 2014 Elsevier B.V. All rights reserved.