XoPercept: Fine-Grained RGB-D Perception and Mapping for Safe Navigation in Wearable Humanoid Exoskeletons
Bibliographic record
Abstract
Lower-limb exoskeletons can restore walking ability for individuals with mobility impairments by augmenting human strength. However, even millimeter-scale obstacles or minor surface irregularities can adversely affect an exoskeleton's stability and hinder real-world deployment. This paper presents XoPercept, an integrated 360° perception system for the selfbalancing XoMotion exoskeleton that employs multiple RGB-D cameras to continuously map the walking surface. In indoor experiments, XoPercept detected$\mathbf{9 8 \%}$of obstacles as small as 1 mm and, using a novel hybrid sizing method, provided 3D measurements of obstacles with errors under 3 cm, thereby generating detailed terrain models. By closing this critical safety gap, XoPercept enhances the reliability and confidence of exoskeleton-assisted mobility in everyday environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".