Evaluating robot navigation behaviour in social environments
Bibliographic record
Abstract
Technology has reached the point where robots are being deployed in public environments.Roboticists are currently making decisions which will dictate how robots look, feel and act in the future.The process of designing robot interactions is based on a dynamic exchange between roboticists, clients and end users.As robots become more capable and gain independence, this exchange shifts towards richer interactions between robot and user.Robots will need to showcase behaviours that adapt to their surroundings whether they be physical or social.In order to do so we first need to identify consistent measurable estimators for people's subjective evaluation of robot behaviour.This thesis presents two experiments which aim at identifying these estimators.In a first experiment, we compare expert evaluations to standard navigation algorithm metrics using videos of a powered wheelchair navigating in a hallway.Our results suggest that these metrics do not fully explain the reactions people have when in presence of robots.In a follow up experiment, we focus on predictable robot behaviour and compare participants reactions to two test cases with different levels of predictability.For this study, we used the QC Bot, a hospital delivery robot.Again, only part of our results could be explained by features extracted from robot sensor data.We conclude that different people have different biases
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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; both teacher heads agree on what is shown here.
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".