A compact underactuated gripper with two fingers and a retractable suction cup
Bibliographic record
Abstract
Modern industrial applications of robotics such as small-series production and automated warehousing require versatile grippers, i.e., grippers that can pick up the widest possible variety of objects. These objects must often be grasped or placed inside a container, which limits the size of the gripper. In this article, we propose to combine the two most popular gripper technologies in order to maximise versatility: finger grippers and suction-cup (vacuum) grippers. Many researchers and a few companies have followed this same idea in the past, but their gripper designs are often overly complex or too bulky to pick up objects inside containers. Here, we develop a gripper where the suction cup is lodged inside the palm of a two-finger robotic hand. The suction cup is mounted on a retractile rod that can extend to pick up objects inside containers without interference from the two fingers. A single actuator drives both the finger and sliding-rod motions so as to minimise the gripper complexity. The opening and closing sequence of the gripper is achieved by using a planetary gear train as transmission between the actuator, the fingers and the suction cup sliding mechanism. Special attention is paid to minimise the overall gripper size; its diameter being kept to 75 mm, which is that of the end link of the common UR5 robot. A prototype of the gripper is built and its versatility is demonstrated in a short accompanying video.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".