Carnegie Mellon University
Viewpoint-Based Legibility Optimization

Stefanos Nikolaidis, Anca Dragan, and Siddhartha Srinivasa
Human-Robot Interaction, March, 2016.

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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.

conveying intent, trajectory optimization, human-robot collaboration

Associated Center(s) / Consortia: Quality of Life Technology Center, National Robotics Engineering Center, and Center for the Foundations of Robotics
Associated Lab(s) / Group(s): Personal Robotics

Text Reference
Stefanos Nikolaidis, Anca Dragan, and Siddhartha Srinivasa, "Viewpoint-Based Legibility Optimization," Human-Robot Interaction, March, 2016.

BibTeX Reference
   author = "Stefanos Nikolaidis and Anca Dragan and Siddhartha Srinivasa",
   title = "Viewpoint-Based Legibility Optimization",
   booktitle = "Human-Robot Interaction",
   month = "March",
   year = "2016",