Towards Transferring Grasping from Human to Robot with RGBD Hand Detection
Bibliographic record
Abstract
The task of transferring human knowledge and capabilities to robots is still an open problem. In this paper, we address the problem of transferring human grasping locations of a particular object to a robot manipulator. Using an RGBD sensor, we propose a computer vision based method for human hand detection. This method implements a pixelwise hand detection method with the Random Forest classification algorithm in the color channel. It also creates a kernel-based hand detection method in the depth channel. Based on the theory of joint probability, it fuses both color and depth cues. As a result, this method is able to deal with noisy background and occlusion. Moreover, we apply this method to a grasping task example. In our test, the robot is able to gain the grasping knowledge from visual observation. Our method is complemented with experimental results on the settings of four different sequences with different level of difficulties, and has achieved high performance with respect to hand detection accuracy in comparison with RGB and Depth only methods.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".