Development and Mobile Deployment of a Stair Recognition System for Human-Robot Locomotion
Bibliographic record
Abstract
Abstract Environment sensing and recognition can improve the safety and autonomy of human-robot locomotion, especially during transitions between environmental states such as walking to and from stairs. However, accurate and real-time perception on edge devices with limited computational resources is an open problem. Here we present the development and mobile deployment of StairNet - a vision-based automated stair recognition system powered by deep learning. Building on ExoNet - the largest open-source dataset of egocentric images of real-world walking environments - we designed a new dataset specifically for stair recognition with over 515,000 images. We then developed a lightweight and efficient convolutional neural network for image classification, which accurately predicted complex stair environments with 98.4% accuracy. We also studied different model compression and optimization methods and deployed our system on several mobile devices running a custom-designed iOS application with onboard accelerators using CPU, GPU, and/or NPU backend computing. Of the designs that we tested, our highest performing system showed negligible reductions in classification accuracy due to the model conversion for mobile deployment and achieved an inference time of 2.75 ms on an iPhone 11. The high speed and accuracy of the StairNet system on edge devices opens new opportunities for autonomous control and planning of robotic prosthetic legs, exoskeletons, and other assistive technologies for human locomotion.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".