Probabilistic Pointing Target Prediction via Inverse Optimal Control

Brian D. Ziebart, Anind Dey, and J. Andrew (Drew) Bagnell
International Conference on Intelligent User Interfaces (IUI 2012) , March, 2012.


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Abstract
Numerous interaction techniques have been developed that make “virtual” pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets.

Keywords
Cursor prediction, probabilistic inference, continuous control

Notes
Number of pages: 10
Note: Best Paper Award Nominee

Text Reference
Brian D. Ziebart, Anind Dey, and J. Andrew (Drew) Bagnell, "Probabilistic Pointing Target Prediction via Inverse Optimal Control," International Conference on Intelligent User Interfaces (IUI 2012) , March, 2012.

BibTeX Reference
@inproceedings{Ziebart_2012_7002,
   author = "Brian D. Ziebart and Anind Dey and J. Andrew (Drew) Bagnell",
   title = "Probabilistic Pointing Target Prediction via Inverse Optimal Control",
   booktitle = "International Conference on Intelligent User Interfaces (IUI 2012) ",
   month = "March",
   year = "2012",
   number= "CMU-RI-TR-",
   Notes = "Best Paper Award Nominee"
}