Learning Motion Predictors for Smart Wheelchair using Autoregressive\n Sparse Gaussian Process
Bibliographic record
Abstract
Constructing a smart wheelchair on a commercially available powered\nwheelchair (PWC) platform avoids a host of seating, mechanical design and\nreliability issues but requires methods of predicting and controlling the\nmotion of a device never intended for robotics. Analog joystick inputs are\nsubject to black-box transformations which may produce intuitive and adaptable\nmotion control for human operators, but complicate robotic control approaches;\nfurthermore, installation of standard axle mounted odometers on a commercial\nPWC is difficult. In this work, we present an integrated hardware and software\nsystem for predicting the motion of a commercial PWC platform that does not\nrequire any physical or electronic modification of the chair beyond plugging\ninto an industry standard auxiliary input port. This system uses an RGB-D\ncamera and an Arduino interface board to capture motion data, including visual\nodometry and joystick signals, via ROS communication. Future motion is\npredicted using an autoregressive sparse Gaussian process model. We evaluate\nthe proposed system on real-world short-term path prediction experiments.\nExperimental results demonstrate the system's efficacy when compared to a\nbaseline neural network model.\n
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".