Arrow Research search

Author name cluster

Bruce N. Walker

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

ICRA Conference 2025 Conference Paper

Do Looks Matter? Exploring Functional and Aesthetic Design Preferences for a Robotic Guide Dog

  • Aviv L. Cohav
  • A. Xinran Gong
  • Joanne Taery Kim
  • Clint Zeagler
  • Sehoon Ha
  • Bruce N. Walker

Dog guides offer an effective mobility solution for blind or visually impaired (BVI) individuals, but conventional dog guides have limitations including the need for care, potential distractions, societal prejudice, high costs, and limited availability. To address these challenges, we seek to develop a robot dog guide capable of performing the tasks of a conventional dog guide, enhanced with additional features. In this work, we focus on design research to identify functional and aesthetic design concepts to implement into a quadrupedal robot. The aesthetic design remains relevant even for BVI users due to their sensitivity toward societal perceptions and the need for smooth integration into society. We collected data through interviews and surveys to answer specific design questions pertaining to the appearance, texture, features, and method of controlling and communicating with the robot. Our study identified essential and preferred features for a future robot dog guide, which are supported by relevant statistics aligning with each suggestion. These findings will inform the future development of user-centered designs to effectively meet the needs of BVI individuals.

Details

IROS Conference 2004 Conference Paper

Map-based priors for localization

  • Sang Min Oh
  • Sarah Tariq
  • Bruce N. Walker
  • Frank Dellaert

Localization from sensor measurements is a fundamental task for navigation. Particle filters are among the most promising candidates to provide a robust and real-time solution to the localization problem. They instantiate the localization problem as a Bayesian altering problem and approximate the posterior density over location by a weighted sample set. In this paper, we introduce map-based priors for localization, using the semantic information available in maps to bias the motion model toward areas of higher probability. We, show that such priors, under a particular assumption, can easily be incorporated in the particle filter by means of a pseudo likelihood. The resulting filter is more reliable and more accurate. We show experimental results on a GPS based outdoor people tracker that illustrate the approach and highlight its potential.

Details
v2026.09.13