User intentions funneled through a human-robot interface

We describe a method for predicting user intentions as part of a human-robot interface. In particular, we show that funnels, i.e., geometric objects that partition an input space, provide a convenient means for discriminating individual objects and for clustering sets of objects for hierarchical tasks. One advantage of the proposed implementation is that a simple parametric model can be used to specify the shape of a funnel, and a straightforward heuristic for setting initial parameter values appears promising. We discuss the possibility of adapting the user interface with machine learning techniques, and we illustrate the approach with a humanoid robot performing a variation of a standard peg-insertion task.