Improved action and path synthesis using Gradient Sampling
Bibliographic record
Abstract
Shortest paths generated through gradient descent on a value function have a tendency to chatter and/or require an unreasonable number of steps to synthesize. We demonstrate that the gradient sampling algorithm of [Burke, Lewis & Overton, 2005] can largely alleviate this problem. For systems subject to state uncertainty whose state estimate is tracked using a particle filter, we propose the Gradient Sampling with Particle Filter (GSPF) algorithm, which uses the particles as the locations in which to sample the gradient. At each step, the GSPF efficiently finds a consensus direction suitable for all particles or identifies the type of stationary point on which it is stuck. If the stationary point is a minimum, the system has reached its goal (to within the limits of the state uncertainty) and the algorithm naturally terminates; otherwise, we propose two approaches to find a suitable descent direction. We illustrate the effectiveness of the GSPF on several examples using the ROS and Gazebo robot simulation environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".