Spatiotemporal Motion Profiles for Cost-Based Optimal Approaching Pose Estimation
Bibliographic record
Abstract
Recent public perceptions indicate a positive shift towards a society with human and robot co-existing, especially aged populations. The ability to socially navigate become crucial for mobile robots by enabling them to guarantee not only human physical safety but also psychological comfort, and enhance robots contextual awareness in human-robot interactions (HRI). In this study, we introduce an extended navigation scheme to approach moving target based on the tracking of human spatiotemporal motion, social studies on proxemics, and kino-dynamics of the mobile robot. The strategy utilizes existing multi-layer cost-based navigation mapping for complete integration with plannings and introduce soft social constraints by extending the costmap value range. The primary contributions include (i) spatio-temporal motion profiles (SMPs) of all humans under tracking, (ii) a social navigation cost function (SNCF) for filtering socially-optimal goal poses. The results drawn from simulated testings across three normative social situations, and statistical analysis demonstrate the SMPs effectiveness through measured spatial and temporal coefficients. The driving factors safety and appropriate social construct are determined to be either statistically or practically significant, while also introducing a complete navigation scheme taking into account of socially acceptable behaviours for the robot.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".