An $L_{0}$-Norm-Based Sparse Projection Neural Network for Cooperative Motion of Dual-Arm Robots Under Physical Constraints
Bibliographic record
Abstract
Sparsification techniques aim to reduce data density by extracting essential features from high-dimensional data, thereby lowering computational and storage demands while enhancing model efficiency and generalization. In the collaborative control of dual-arm robots, promoting sparsity in joint-angle velocities helps minimize the number of active joints, thereby reducing energy consumption and the risk of collisions. Although the$L_{0}$-norm provides an exact measure of sparsity by counting nonzero elements, its minimization is an NP-hard problem. Therefore, recent studies have employed alternative norms (e.g., the$L_{1}$-norm) to promote sparsity in joint-angle velocities. However, these alternatives often struggle to achieve consistently high levels of sparsity. To overcome this limitation, a novel$L_{0}$-norm-based sparse projection neural network (LS-PNN) is proposed for dual-arm robotic cooperation under physical constraints. Unlike existing approaches, the LS-PNN preserves the sparsity representation accuracy of the$L_{0}$-norm while avoiding its inherent NP-hard problem. The stability of the LS-PNN is theoretically verified. Simulation and physical robot experiments demonstrate that our proposed LS-PNN significantly outperforms other state-of-the-art schemes when dealing with the sparsity of joint-angle velocities under handling constraints.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".