Development of a Pillow Placement Process for Robotic Bed-Making
Bibliographic record
Abstract
Abstract Bed-making is a common chore completed in various living environments to promote user comfort, hygiene, and well-being. Unfortunately, the physical and tedious nature of the act makes it challenging for segments of the elderly community to complete the chore, and thus the opportunity arises to develop robots to automate the task. However, despite the opportunity’s importance and positive impact, there is limited research on developing robotic bed-making systems. The aim of this research is to start addressing this gap by proposing methods for accomplishing pillow placement, a major part of the bed-making task. This paper introduces a pillow placement process to be used by a static 6-DOF (degree of freedom) one-armed robotic manipulator equipped with a 2-finger gripper. The process uses YOLOv4-tiny, image transformations, and principal component analysis (PCA) to infer pillow poses in a transformed RGB image, as well as a set of manipulator macro-actions to move pillows to their goal pose. We evaluated the proposed methodology in a real-world setting, where it enabled the robot to place pillows at desired poses on a miniature bed successfully in 89% of the experimental runs.
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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".