Robotic pick-and-handover maneuvers with camera-based intelligent object detection and impedance control
Bibliographic record
Abstract
A vision-based impedance control method is applied to a 7-degree-of-freedom Franka Emika (FE) Panda robotic manipulator to complete pick-and-handover tasks with human-like grasping of fruits. The interfaces between different hardware, controllers, and path planners are developed using robot operating systems (ROS). An RGB camera is applied using the YOLOv5 (You Only Look Once v5) object detection algorithm, which is trained for various fruits and human hands and then employed for identifying the location of the targeted objects. A qb-SoftHand robotic hand is used as the end-effector for the grasping tasks. By integrating these components together with ROS, the FE Panda robot successfully achieves autonomous human-like handover tasks, as shown in the experimental results.
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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) | 0.000 | 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 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".