FORESEER: Recognize and utilize uncertainties by integrating data-based learning and symbolic feedback
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Uncertainties resulting from intricate internal model uncertainties and external environmental disturbances significantly degrade robot planning and control performance. However, recognizing such persistently varying uncertainties in an explainable and lightweight manner is exceptionally challenging. We present two converged uncertainty prediction frameworks through the Fusion of Online Reactive Estimation and Sustained Experience Exploitation for Robots (FORESEER), enabling accurate prediction of two general kinds of uncertainties. Both frameworks feature properties of precision, lightweight, universality, and stability, in comparison with existing solutions. At first, a prediction algorithm for nonlinearly parametric uncertainties is developed by merging analytical basis learning with online symbolic adaptive estimation. Furthermore, an online prediction algorithm for more challenging composite uncertainties is proposed by seamlessly integrating learning-based feedforward and model-based/symbolic feedback observer. Benchmark comparisons on flying drones showcase the accuracy of the FORESEER on various real uncertainties including mass, aerodynamic drag, rain, and rope tension, leading to subsequent high-precision control. Moreover, an energy-saving and time-saving planning strategy is presented by utilizing the favorable wind. The developed algorithms hold the promising potential for direct combination with existing planning/control algorithms, promoting the environmental adaptability of robots.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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