Application of Simulated Annealing to Dexterous Object Manipulation With Multiple Agents
Bibliographic record
Abstract
Abstract The manipulation task (called object re-configuration problem) is stated as the following: given an initial grasp of the object find the motion’s trajectories of the agents to move the object to the desired configuration. In general collision free paths for all agents must be found toward the contact points on the object (pre-grasp configuration) and the grasping and manipulation forces should then be exerted on the object by the agents. These forces are determined first to ensure a stable grasp, then to manipulate the object. We present a model based motion planner algorithm for manipulating agents for object re-configuration using simulated annealing (SA) algorithm for generating the relative motion between the object and agents. The motion of the agents relative to the object can be pure sliding, pure rolling, breaking contact and agent relocation is allowed. The motion sequence is represented by a relative velocity matrix. The algorithm can be used for example to move a known shaped object to a different position and orientation with a robotic hand. Simulation results are presented.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".