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Viewpoint-based legibility optimization | IEEE Conference Publication | IEEE Xplore

Viewpoint-based legibility optimization


Abstract:

Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the stron...Show More

Abstract:

Much robotics research has focused on intent-expressive (legible) motion. However, algorithms that can autonomously generate legible motion have implicitly made the strong assumption of an omniscient observer, with access to the robot's configuration as it changes across time. In reality, human observers have a particular viewpoint, which biases the way they perceive the motion. In this work, we free robots from this assumption and introduce the notion of an observer with a specific point of view into legibility optimization. In doing so, we account for two factors: (1) depth uncertainty induced by a particular viewpoint, and (2) occlusions along the motion, during which (part of) the robot is hidden behind some object. We propose viewpoint and occlusion models that enable autonomous generation of viewpoint-based legible motions, and show through large-scale user studies that the produced motions are significantly more legible compared to those generated assuming an omniscient observer.
Date of Conference: 07-10 March 2016
Date Added to IEEE Xplore: 14 April 2016
ISBN Information:
Electronic ISSN: 2167-2148
Conference Location: Christchurch, New Zealand

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