Game Research at McGill Navigating Social Spaces
Bibliographic record
Abstract
Behaviour of robots within a human-populated space can be disruptive, as robot motion does not necessarily conform to social norms. Typical movement models are oblivious to social expectations, and so easily violate personal space and other social rules, magnifying the unnatu-ral behaviour of robot agents and causing discomfort to human occupants. This paper presents a navigation algorithm that incorporates human proxemics into a modified Rapidly-exploring Random Tree (RRT) algorithm. Our Socially-Realistic RRT algorithm (SRRRT) includes both a cost function based on a realistic model of human interaction distances, as well as a human motion model in order to produce movement patterns that better integrate with human so-cial behaviour. We experiment with our algorithm in simulation, comparing it with both a naive RRT and an A * implementation in both static and dynamic movement contexts. SRRRT demonstrates quantifiably better paths in terms of social cost, while maintaining a simple and easily extensible implementation design. Inclusion of such a design in robot motion enables more socially transparent behaviour, improving the ability of humans and robots in real or virtual contexts to coexist. 1
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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".