Geoffrey A Hollinger
Biographic Data
| ID | 10123212 |
|---|---|
| NAME | Geoffrey A Hollinger |
| GIVEN NAMES | Geoffrey A |
| FAMILY NAME | Hollinger |
| SIGNATURE | HOLLINGER G A |
| AFFILIATIONS | Oregon State University |
| ORCID | 0000-0001-6502-8950 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Explainable and Adaptable Robotic Decision Making for Scientific Data Collection
In this article, we investigate using explanations to improve decision-making for robotic scientific data collection missions. We propose the Preference Elicitation with contrasting Feature-based eXplanations (PrEFeX) method, which combines preferences with contrasting explanations focused on a single explanatory feature. We first provide a verification of the contrasting explanations by themselves using a planner prediction user study with 16 ex…
GoBot
In early motor interventions from clinical rehabilitation to physical activity encouragement, one major challenge is maintaining child engagement and motivation. Robots show unique promise for addressing this challenge, but providing robots with new types of autonomous functionality is vital for promoting robot integration and usefulness in the clinic and home spaces. To provide needed autonomy capabilities for GoBot, our assistive robot for chil…
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GoBot
In early motor interventions from clinical rehabilitation to physical activity encouragement, one major challenge is maintaining child engagement and motivation. Robots show unique promise for addressing this challenge, but providing robots with new types of autonomous functionality is vital for promoting robot integration and usefulness in the clinic and home spaces. To provide needed autonomy capabilities for GoBot, our assistive robot for chil…
Explainable and Adaptable Robotic Decision Making for Scientific Data Collection
In this article, we investigate using explanations to improve decision-making for robotic scientific data collection missions. We propose the Preference Elicitation with contrasting Feature-based eXplanations (PrEFeX) method, which combines preferences with contrasting explanations focused on a single explanatory feature. We first provide a verification of the contrasting explanations by themselves using a planner prediction user study with 16 ex…
AI-based Problem Solving and Planning (1 work) · Artificial Intelligence (1 work) · Computer Science (1 work) · Context-Aware Activity Recognition Systems (1 work) · Data collection (1 work) · Explainable Artificial Intelligence (XAI (1 work) · Human-Automation Interaction and Safety (1 work) · Human–computer interaction (1 work) · Planner (1 work) · Preference (1 work)