VISUAL SERVOING IN VIRTUALISED ENVIRONMENTS BASED ON OPTICAL FLOW LEARNING AND CONSTRAINED OPTIMISATION, 1-10.
Bibliographic record
Abstract
In this paper, we describe a visual servoing method for object picking.We propose a new architecture for generating robotic manipulator motions approaching a target object for grasping.The architecture consists of two convolutional neural networks (CNNs), one generating goal-directed motion and one collision avoidance motion.The networks' outputs are combined, along with additional constraints, such as motion ranges of the joints, by means of quadratic programming (QP).One issue with learning-based approaches is that large amounts of training data are required.We devise an operation strategy that reduces the amount of training data required using a physics simulator.This method enables visual servoing that is unaffected by texture and colour variation in real environments.We show the effectiveness of the proposed method in experiments using simple shapes as target objects.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".