Synergistic Optimization of Humanoid Robot Arm Configuration and Flexible Vision Measurement and Calibration System
Bibliographic record
Abstract
This study concentrates on two primary challenges in the optimization of humanoid robotic arm configurations and the calibration of flexible visual measurement systems, with the goal of improving the motion adaptability and measurement accuracy of robotic systems. In terms of configuration design, we propose a method for screening candidate configurations based on motion flexibility analysis, which incorporates principles from both human anatomy and robotics. By generating performance distribution charts for candidate configurations and comparing them with human arm movement characteristics and workspace parameters, we ultimately identify the most compatible serial robotic arm configuration, establishing a foundation for subsequent motion planning. Regarding calibration optimization, we devise an improved strategy to address the limitations of existing methods. This strategy establishes a joint correction model for hand-eye relationship errors and kinematic parameter deviations, utilizing a linear structured light sensor mounted on the end-effector and fixed reference constraints. Through iterative algorithms that enhance calibration precision, it maximizes the system's potential for high-precision robotic operations. The research offers theoretical and technical support for the synergistic optimization of intelligent control in humanoid robotic arms and high-precision visual measurement systems, demonstrating significant engineering applicability.
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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 | 0.005 |
| 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 | 0.001 |
| 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".