Real-Time Interactive Capabilities of Dual-Arm Systems for Humanoid Robots in Unstructured Environments
Bibliographic record
Abstract
The efficient and rapid processing of target positions is crucial for the real-time interactive motion operations of humanoid robots within unstructured acquisition. This paper presents a novel strategy for the acquisition and transfer of target positions, optimizing the coordination of dual-arm systems in humanoid robots, essential for improving grasping speed and environment interaction responsiveness. The proposed approach integrates a camera mounted on the observer arm for real-time target detection, alongside an executor arm responsible for performing interactive tasks. The mechanism performs seamless and swift target acquisition of positions, converting them from the observer to the executor arm’s operational frame. We utilize YOLOv8 for instant target object detection in RGB images, coupled with depth image analysis for accurate object interaction. A calibration method, enhanced by a plus sign median filter (PSMF), is introduced to improve the accuracy and precision of the depth data. Additionally, a novel kinematic technique is proposed to expedite the position transfer process. Simulation and experimental validations show the efficiency of our kinematic approach, which is demonstrated to be nearly 50 times faster than traditional methods. Moreover, the PSMF calibration technique significantly elevates the robot’s grasping response and success rate, showing that the proposed methods augment the interactive capabilities of humanoid robots in dynamic settings.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".