Explainable and Adaptable Robotic Decision Making for Scientific Data Collection
Bibliographic Data
| ID | 22190771 |
|---|---|
| Authors | Ian C Rankin (0000-0003-3792-4453, Oregon State University), Thane Somers (0000-0001-7881-3878, Transnetyx Inc., Cordova, Tennessee, USA), Sean Buchmeier (0009-0006-5031-4368, Oregon State University), Alivia M Eng (0009-0006-3570-1087, Georgia Institute of Technology), Cristina G Wilson (0000-0002-9639-2752, Oregon State University), Geoffrey A Hollinger (0000-0001-6502-8950, Oregon State University) |
| Year | 2026 |
| Volume | 15 |
| Issue | 4 |
| Pages | 1-38 |
| Publication date | 2026-07-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Journal identifiers | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Publisher | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3811028 |
| OpenAlex | W7154961709 |
| Language | EN |
| References cited | 55 |
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 expert participants (Phase 1). This study showed that there was no increase in understanding of robot plans using contrasting explanations without preferences. To elucidate what information the autonomous measurement selection system was missing to be useful, we interviewed 4 planetary scientists and 2 oceanographers (Phase 2). We found that scientists focused heavily on understanding the objectives of the system and wanted explanations that (1) grounded the explanations to tradeoffs the system made, (2) connected the explanations to an ability to modify the behavior of the decision making, and (3) attached the explanation system’s features to comparable features the scientists considered. To this end, we propose combining explanations with user preference learning of the reward function in an iterative design process of the measurement plans with scientists in the loop. We tested our proposed preference and explanation system in a field deployment with planetary scientists on Mt. Hood, Oregon, and performed a post-data collection survey on the quality of the plans with 22 experts (Phase 3). We found the experts preferred the measurement plan selected by our proposed PrEFeX method over a baseline without explanations or preferences
Data collection · Planner · Preference · Preference elicitation · AI-based Problem Solving and Planning · Explainable Artificial Intelligence (XAI · Human-Automation Interaction and Safety
Explanation in artificial intelligence
Interpreting Black-Box Models
From local explanations to global understanding with explainable AI for trees
Random Forests
Evaluating Effect Size in Psychological Research
Communicating Missing Causal Information to Explain a Robot’s Past Behavior
The Need for Verbal Robot Explanations and How People Would Like a Robot to Explain Itself
Batch Active Learning of Reward Functions from Human Preferences
| Citation velocity | historical |
|---|---|
| Highly cited | No |