Online Evaluation for Learning Feasible Robotic Grasps With Physical Constraints
Bibliographic record
Abstract
Existing grasp planning networks often learn from labeled images with grasp examples to eliminate the need for training through physical grasp attempts. As a result, trained networks lack an understanding of the physical constraints involved in successful grasps, leading to infeasible predictions and inaccurate evaluation. In this article, we propose a framework for integrating physical constraints, e.g., collision avoidance, into grasp learning through on-line grasp evaluation. During training, the proposed framework initially evaluates the feasibility of network predictions using physical constraints. Subsequently, physical supervision is generated based on both the feasible predictions and the geometries of the objects. In this manner, the network learns from its real-time errors and the object shape, in addition to labeled data. Experimental results demonstrated that our evaluation method achieved a significantly lower false rate (5.5%) than the commonly used metrics (intersection over union: 19.0%, SGT: 17.5%). Furthermore, the proposed framework effectively improves the network's real-world grasping success rate on EGAD objects by 18.7% for isolated objects (2450 attempts) and 15.8% for cluttered scenes (331 attempts). These results highlight the effectiveness of integrating physical constraints for feasible grasp prediction and accurate evaluation.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".