Robot Evidence Based Search for a Dynamic User in an Indoor Environment
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we present the development of an evidence-based search planner for a mobile assistive robot to autonomously search for a dynamic person in a multi-room home environment in order to provide assistance. We solve the dynamic person search problem by uniquely considering evidence of household objects along with a user spatial-temporal model to increase the probability of finding the user. Our planner utilizes a Partially Observable Markov Decision Process (POMDP) to plan optimal robot search paths in the environment as the user and evidence locations are partially observable. Extensive simulated experiments in a home environment were conducted to compare our proposed evidence-based search approach with 1) a search technique without prior user information, and 2) a search technique that only uses a user model. The results show that our proposed search technique has higher success rates for finding the user and is more robust to the dynamic behaviors of the user.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it