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Understanding Human Intentions via Hidden Markov Models in Autonomous Mobile Robots
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BibTeX
@MISC{Kelley_understandinghuman,
author = {Richard Kelley and Monica Nicolescu and Alireza Tavakkoli and Mircea Nicolescu and Christopher King and George Bebis},
title = {Understanding Human Intentions via Hidden Markov Models in Autonomous Mobile Robots},
year = {}
}
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Abstract
Understanding intent is an important aspect of communication among people and is an essential component of the human cognitive system. This capability is particularly relevant for situations that involve collaboration among agents or detection of situations that can pose a threat. In this paper, we propose an approach that allows a robot to detect intentions of others based on experience acquired through its own sensory-motor capabilities, then using this experience while taking the perspective of the agent whose intent should be recognized. Our method uses a novel formulation of Hidden Markov Models designed to model a robot’s experience and interaction with the world. The robot’s capability to observe and analyze the current scene employs a novel vision-based technique for target detection and tracking, using a non-parametric recursive modeling approach. We validate this architecture with a physically embedded robot, detecting the intent of several people performing various activities. Categories and Subject Descriptors I.2.9 [Artificial Intelligence]: Robotics – autonomous vehicles, operator interfaces, sensors.
Keyphrases
hidden markov model autonomous mobile robot human intention human cognitive system essential component several people various activity non-parametric recursive modeling approach robotics autonomous vehicle sensory-motor capability current scene novel vision-based technique subject descriptor operator interface target detection important aspect novel formulation robot capability robot experience artificial intelligence
