“To Approach Humans?”: A Unified Framework for Approaching Pose Prediction and Socially Aware Robot Navigation
Bibliographic record
Abstract
We propose a unified framework for approaching pose prediction, and socially aware robot navigation, which enables a mobile service robot to safely and socially approach a dynamic human or human group in a social environment. The proposed framework is composed of four major functional blocks: 1) human detection and human features extraction to estimate the human states, and the social interaction information from the socio-spatio-temporal characteristics of a human and a group of humans; 2) a dynamic social zone (DSZ) consisting of an extended personal space and a social interaction space is modeled by the human states and social interaction information to represent space around the human and human group; 3) the approaching pose of the robot to a human or a human group is predicted using the DSZ and the environmental surroundings; and 4) the DSZ and the estimated approaching pose are incorporated into a motion planning system, comprising a local path planner and dynamic window approach technique, to generate the motion control commands for the mobile robot. We evaluate the developed framework through both simulation and real-world experiments under the newly proposed human safety and comfort indices, including the social individual index, social group index, and social direction index. The results show that the unified framework is fully capable of driving a mobile robot to approach both stationary and moving humans and human groups in a socially acceptable manner while guaranteeing human safety and comfort.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".