Quantifying Impact of Robot Perception Accuracy at Landmarks in Decision-Making during Complex Situations
Bibliographic record
Abstract
Perception and decision making is contextual. Landmarks are components of the environment associated with high perception and localization accuracy and their presence can significantly impact agent beliefs and decisions. Our research focuses on integrating state-of-the-art sensing technologies to enhance human decision-making. The perception model incorporates multi-sensor fusion, utilizing LiDAR, cameras, and inertial sensors to create a dynamic representation of the environment. Object recognition and tracking algorithms further enable the robot to interpret the scene providing valuable insights for informed decision-making. This effort involves a novel perception model tailored for mobile robots, emphasizing its role in assisting humans during decision-making processes. Our preliminary model includes multi-sensor fusion, semantic scene analysis, and understanding, evaluated using existing SLAM datasets. In that objective, a mobile robot serves as a valuable companion in helping navigation by providing timely and relevant information. An initial stage, results, and evaluation of our perception model are detailed in this paper. In this aspect, we validate the contextual state by object detection. In the context of achieving localization without GPS in a network of roads using stratified sequential importance sampling where the stratification levels are based on semantic object spaces in the map and on the running time, we quantify the impact of landmark presence and frequency on the success of localization and thereby of decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".