Bibliographic record
Abstract
We begin our journey into state estimation by considering systems that can be modelled using linear equations corrupted by Gaussian noise. While these linear-Gaussian systems are severe approximations of real robots, the mathematics are very amenable to straightforward analysis. We discuss the difference between Bayesian estimation and maximum a posteriori estimation in the context of batch trajectory estimation; these two approaches are effectively the same for linear systems, but this contrast is crucial to understanding the results for nonlinear systems later on. After introducing batch trajectory estimation, we show how the structure of the problem gives rise to sparsity in our equations that can be exploited to provide a very efficient solution. Indeed, the famous Rauch-Tung-Striebel smoother (whose forward pass is the Kalman filter) is equivalent to solving the batch trajectory problem. Several other avenues to the Kalman filter are also explored. Although much of the book focusses on discrete-time motion models for robots, we show how to begin with continuous-time models as well; in particular, we make the connection that batch continuous-time trajectory is an example of Gaussian process regression, a popular tool from machine learning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".