Maximizing the Throwing Distance of Robotic Manipulators: An Optimization Approach
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
Abstract Manipulators are increasingly being called upon to perform a wide range of tasks. This paper explores the maximal distance throwing task for robotic manipulators and shows that this characteristic can be incorporated in the kinematic design process. Indeed, knowing the maximum distance that a manipulator can throw objects is useful in determining the viability of certain throwing tasks it might be called upon to execute. This paper studies three optimization problems: optimizing the release state to maximize the throwing distance, optimizing the kinematic trajectory subject to position, velocity, acceleration, and jerk constraints, and finally optimizing the kinematic design of manipulators to maximize the workspace as well as the throwing distance. Three manipulator architectures are used as case studies for these optimizations: a planar RR, a spatial RRR, and a wrist-partitioned 6R manipulator.
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 | 0.000 | 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