CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A review and taxonomy of distortion-oriented presentation techniques
ACM Transactions on Computer-Human Interaction (TOCHI)
Improving focus targeting in interactive fisheye views
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Robot Learning From Demonstration
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Towards adjustable autonomy for the real world
Journal of Artificial Intelligence Research
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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.