Real-Time Posture Control for a Robotic Manipulator Using Natural Human–Computer Interaction
Bibliographic record
Abstract
In this paper, we propose a vision-based recognition approach to control the posture of a robotic arm with three degrees of freedom (DOF) using static and dynamic human hand gestures. Two different methods are investigated to intuitively control a robotic arm posture in real-time using depth data collected by a Kinect sensor. In the first method, the user’s right index fingertip position is mapped to compute the inverse kinematics on the robot. Using the Forward And Backward Reaching Inverse Kinematics (FABRIK) algorithm, the inverse kinematics (IK) solutions are displayed in a graphical interface. Using this interface and his left hand, the user can intuitively browse and select a desired robotic arm posture. In the second method, the user’s left index position and direction are respectively used to determine the end-effector position and an attraction point position. The latter enables the control of the robotic arm posture. The performance of these real-time natural human control approaches is evaluated for precision and speed against static and dynamic obstacles.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".