Quantitative human and robot motion comparison for enabling assistive device evaluation
Why this work is in the frame
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Bibliographic record
Abstract
A promising new application area for humanoid robots is in the area of assistive device testing. Humanoid robots can facilitate the experimental evaluation of assistive devices by providing repeatable, measurable, human-like motion while removing the difficulties and potential safety hazards associated with human trials. To ensure that the humanoid robot is providing a valid test platform, the robot must move in a similar way as a human while wearing the assistive device. This challenge is made difficult due to the inherent variability in human motion. In this paper, we propose an approach for a quantitative comparison between human and robot motion that identifies both the difference magnitude and the difference location, and explicitly handles both spatial and temporal variability of human motion. The proposed approach is demonstrated on data from robot gait and sagittal plane lifting.
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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 | 0.000 |
| Open science | 0.000 | 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