Human Motion Behaviour Aware Planner (HMBAP) for path planning in dynamic human environments
Why this work is in the frame
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Bibliographic record
Abstract
For a robot navigating in a human inhabited dynamic environment, the knowledge of how the robot's movement can influence the trajectory of people around it can be very valuable. In this work we present a Human Motion Behaviour Aware Planner (HMBAP) which incorporates a Human Motion Behaviour Model (HMBM) in its planning stage to take advantage of this. HMBM is an obstacle avoidance model for people based on social forces which gives the robot an understanding of how people would react to its planned path. This information is useful for the robot to avoid imminent collisions with people in constricted spaces. The resulting robot behaviour is similar to how a human would move (in terms of avoidance behaviour) in a similar situation. We believe that this is a desirable feature for a robot navigating in a human inhabited environment. Our method shows good human-like navigation behaviour in situations where past methods fail to find a solution.
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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