Knowledge-Based Grasp Planning Using Dynamic Self-Organizing Network
Bibliographic record
Abstract
Category-based methods for task-specified grasp planning have recently been proposed in the literature. Such methods, however, are normally time consuming in both training and grasp determination process and lack capabilities to improve grasping skills due to the fixed training data set. This paper presents an improved approach for knowledge-based grasp planning by developing a multi-layer network using self-organizing map. A number of grasp candidates are learned in the experiments and the information that is associated with these grasp candidates is clustered based on different criteria on each network layer. A codebook which is composed of a small number of generalized models and the corresponding task-oriented grasps is generated from the network. In addition, the proposed network is capable of automatically adjusting its size so that the codebook can be continuously updated from each interaction with the novel objects. In order to increase the accuracy and convergence rate of the clustering process, a new initialization method is also proposed. Simulation results present the advantages of the proposed initialization method and the auto-growing algorithm in terms of accuracy and efficiency over some conventional methods. Experimental results demonstrate that novel objects can be successfully grasped in accordance with desired tasks using the proposed approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".