Learning dynamical representations of tools for tool-use recognition

We consider the problem of representing and recognising tools, a subset of objects that have special functionality and action patterns. Our proposed framework is based on the biological evidence of hierarchical representation of tools in the region of the human cortex that generates action semantics. It addresses the shortfalls of traditional learning models of object representation applied on tools. To showcase its merits, this framework is implemented as a hybrid model between the Hierarchical Attentive Multiple Models for Execution and Recognition of Actions Architecture (HAMMER) and Hidden Markov Model (HMM) to recognise and describe tools as dynamic patterns at symbolic level. The implemented model is tested and validated on two sets of experiments of 50 human demonstrations each on using 5 different tools. In the experiment with precise and accurate input data, the cross-validation statistics suggest very robust identification of the learned tools. In the experiment with unstructured environment, all errors can be explained systematically.