Learning-aided state estimation for robotic rollators with experimental validation
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Bibliographic record
Abstract
While demand for assistive technology has risen with aging populations and concomitant increase in mobility disabilities, conventional (passive) walker designs have demonstrated safety and usability limitations. Robotic rollators (or 4-wheeled walkers) have been proposed to address concerns, including slip, fall, and collision risks. To develop control systems for robotic rollators, accurate estimation of the states is required. While model-based estimation approaches have been widely investigated for mobile robots, robotic rollators present unique challenges due to model parameter changes and uncertainties. In contrast, data-driven estimation approaches require sufficient excitation modes during learning to address corner cases. The proposed learning-aided state estimation (L-ASE) method augments an unscented transformation observer with a long short term memory (LSTM) based learning algorithm to estimate rollator states by using on-board inertial measurement unit data and wheel speeds. The stability and boundedness of the error covariance is investigated. The developed learning-aided estimation method is also experimentally verified for the walker-assisted gait and demonstrates superior performance using a robotic rollator platform in rigorous testing conditions. • A hybrid (learning-aided) estimation framework is proposed in this paper which augments an optimal variance Kalman-based filter with deep learning components to address model uncertainties for speed estimation. • In this regard, the major contributions and highlights of this paper can be summarized as follows: (i) A data-driven LSTM based state estimator was developed based on the robot kinematics measured using a low-cost IMU and encoders; (ii) A learning-aided state estimation approach (L-ASE) was developed by augmenting an unscented Kalman based observer with the LSTM estimates; and (iii) Experimental evaluation of both approaches using a custom robotic rollator platform.
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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