Kinect-based robotic manipulation: From human hand to end-effector
Bibliographic record
Abstract
This paper presents a novel robot motion control method in real-time using the Kinect-based hand tracking. Making use of the middleware NiTE2 modules for Kinect, the 3D position of a hand center point can be captured steadily, which is applied to control the position of the robot's end-effector. Meanwhile, an effective method is presented to estimate another two fingertip points in 3D space by processing hand depth images from Kinect. By utilizing these three points (a hand center and two fingertips) a coordinate system can be built up easily, which is used to specify the hand pose. In the same way, the hand pose is used to control the orientation of the end-effector. What's more, the opening and closing actions of gripper are controlled by changing the distance between these two fingertips. This method enables an operator to control the end-effector easily with only one hand in actual real time. The effectiveness and efficiency of the presented method have been verified in the water-pouring tasks with a 5-DOFs modular manipulator.
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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) | 0.001 | 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 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".