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Carnegie Mellon Robotics Institute

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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) , February, 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 = "February",
   year = "2012",
   number= "CMU-RI-TR-",
   Notes = "Best Paper Award Nominee"
}

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