Robot exploration in unknown cluttered environments when dealing with uncertainty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of autonomous robots in urban search and rescue (USAR) missions has many potential benefits in terms of assisting rescue workers and increasing efficiency in these time- critical environments. However, the cluttered and unknown nature of these environments introduces uncertainty in both the sensing and actuation capabilities of a rescue robot. Such uncertainty has not been directly incorporated into the modeling of the USAR problem for existing robots. In this paper, we present the novel use of a partially observable Markov Decision Process (POMDP) method which directly incorporates uncertainty within the decision-making layer of the controller for a rescue robot. A hierarchical task structure is used to decompose the overall exploration and victim identification task of a robot into smaller subtasks. These subtasks are modeled as POMDPs taking into account sensory and actuation uncertainty. Our proposed approach was tested in numerous experiments in unknown and cluttered USAR-like environments. The results should that the approach was able to successfully explore the environments and find victims, while dealing with sensor and actuator uncertainty.
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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 | 0.000 | 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