Path planning using learned constraints and preferences
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
In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of probabilistic constraints to a "raw" input path. For example, a user can sketch an approximate path for a robot without considered issues such as bounded radius of curvature and our system would then elaborate it to include such a constraint. In our approach, the constraints that generate a path are learned by capturing the statistical properties of a set of training examples using supervised learning. Each training example consists of a pair of paths: an unconstrained (raw) path and an associated preferred path. Using a Hidden Markov Model in combination with multi-scale methods, we compute a probability distribution for successive path segments as a function of their context within the path and the raw path that guides them. This learned distribution is then used to synthesize a preferred path from an arbitrary input path by choosing some mixture of the training set biases that produce the maximum likelihood estimate. We present our method and applications for robot control and non-holonomic path planning.
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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