Michael E. Walker
University of Colorado Boulder
16 Papers
11 Citations
Michael E. Walker is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Augmented reality & Computer science. The author has an hindex of 5, co-authored 12 publications.
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Papers
Communicating Robot Motion Intent with Augmented Reality
Michael E. Walker,Hooman Hedayati,Jennifer K. Lee,Daniel Szafir +3 more
- 26 Feb 2018
TL;DR: A new design space for communicating robot motion in-tent is explored by investigating how augmented reality (AR) might mediate human-robot interactions and developing a series of explicit and implicit designs for visually signaling robot motion intent using AR.
260
Robot teleoperation with augmented reality virtual surrogates
Michael E. Walker,Hooman Hedayati,Daniel Szafir +2 more
- 11 Mar 2019
TL;DR: This work explores how advances in augmented reality (AR) may enable the design of novel teleoperation interfaces that increase operation effectiveness, support the user in conducting concurrent work, and decrease stress, and presents two AR interfaces using such a surrogate: one focused on real-time control and one inspired by waypoint delegation.
126
Designing for Depth Perceptions in Augmented Reality
Catherine Diaz,Michael E. Walker,Danielle Albers Szafir,Daniel Szafir +3 more
- 01 Oct 2017
TL;DR: Two experiments using a perceptual matching task are conducted to understand how shading, cast shadows, aerial perspective, texture, dimensionality, and billboarding affected participant perceptions of virtual object depth relative to real world targets.
106
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction: A Survey and Virtual Design Element Taxonomy
TL;DR: A novel taxonomic framework for different types of VAM-HRI interfaces is presented, composed of four main categories of virtual design elements (VDEs) and explained how its elements have been developed over the last 30 years as well as the current directions Vam-Hri is headed in the coming decade.
Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails
TL;DR: This paper uses synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors, and demonstrates the potential of virtual-to-real-world transfer learning.
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