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Marina Obata

dblp:404/6021 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 91% Collaborative and social computing · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction › service robot
delivery robot
0.912025
Look Further: Socially-Compliant Navigation System in Residential Buildings · HRI 2025
Human-robot interaction
mobile robot
0.912025
Look Further: Socially-Compliant Navigation System in Residential Buildings · HRI 2025
Human-robot interaction
robot navigation
0.912025
Look Further: Socially-Compliant Navigation System in Residential Buildings · HRI 2025
Collaborative and social computing › collaborative virtual environments › social virtual reality
personal space
0.312025
Look Further: Socially-Compliant Navigation System in Residential Buildings · HRI 2025

Methods — techniques the papers use, named apart from their topics

user study · 0.9
YearPublicationVenuePosition
2025 Look Further: Socially-Compliant Navigation System in Residential Buildings
abstract
The distance at which a mobile robot reacts to a person strongly impacts various qualities of the human-robot interaction. In this paper, we focus on the navigation of a mobile delivery robot platform in a residential indoor hallway environment. Social navigation methods typically focus on avoiding uncomfortable human-robot interactions, such as when a robot encroaches on someone's personal space. Since personal space has been shown to be in the range of just a few meters, social navigation methods typically focus on deconflicting and resolving these short-range interactions. In this work, however, we demonstrate that by extending the reaction distance to over eight meters, far beyond the typical interaction distance, we can improve the human's perception of the robot's motion. We introduce the Proactive Lane-Changing (PLC) motion pattern and a navigation system that leverages it to react to people at an increased distance. This pattern consists of changing the robot's lateral position as it navigates down the hallway from the center to the side at an eight-meter distance from an oncoming person. We conducted a user study with 42 participants to assess their impressions of the delivery robot based on three service objectives: safety, smoothness, and politeness. In the straight hallway scenario (Frontal Approach), results showed significant improvement in each of these three objectives compared to typical motion patterns found in the literature: slowing down, stopping, and reactive collision avoidance in the proximity of a person. In contrast, in the intersection (Blind Corner) scenarios, none of the approaches performed significantly better than any other, with participants having a diverse range of preferences among robot motion patterns.
Akira Shiba, Marina Obata, Nathan Kau, Zoltán Beck, Rishi Shah, Michael Sudano, Sabrina Lee
HRI2