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.

Measures of dynamic manipulability summarize a manipulator's capacity to generate accelerations for arbitrary tasks, and such measures are useful tools for the design and control of general-purpose robots. Existing measures, however, downplay the effects of velocity or else ignore them altogether. In this paper we derive the relationship between joint velocity and end-effector acceleration, and through case studies we demonstrate that velocity has a complex, non-negligible effect on manipulability. We also provide evidence that movement near a singularity is beneficial for certain tasks.