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Carnegie Mellon Robotics Institute

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Abstract

We present PROCAB, an efficient method for Probabilistically Reasoning from Observed Context-Aware Behavior. It models the context-dependent utilities and underlying reasons that people take different actions. The model generalizes to unseen situations and scales to incorporate rich contextual information. We train our model using the route preferences of over 25 taxi drivers demonstrated in over 100,000 miles of collected data, and demonstrate the performance of our model inferring: (1) decision at next intersection, (2) route to known destination and (3) destination given partially traveled route.

Keywords

Inverse Optimal Control, Probabilistic Reasoning, Navigation, Machine Learning

Notes

Sponsor: NSF

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Quality of Life Technology Center

Associated Lab(s) / Group(s): Human-Robot Interaction Group

Associated Project(s): Quality of Life Technology

Text Reference

Brian D. Ziebart, Andrew Maas, Anind Dey, and J. Andrew (Drew) Bagnell, "Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior," UBICOMP: Ubiquitious Computation, 2008.

BibTeX Reference

@inproceedings{Ziebart_2008_6201,
   author = "Brian D. Ziebart and Andrew Maas and Anind Dey and J. Andrew (Drew) Bagnell",
   title = "Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior",
   booktitle = "UBICOMP: Ubiquitious Computation",
   year = "2008",
}

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