Bibliographic record
Abstract
ABB’s YuMi is a unique, innovative cobot but it is also one of the most challenging 7-degree-of-freedom (DOF) robot arms on the market, in terms of kinematics. Indeed, unlike some other 7-DOF robot arms with revolute joints, in YuMi, consecutive joint axes are normal to each other, but not intersecting. And despite being invented over a decade ago, there is surprisingly little information available about its kinematics. To effectively incorporate this manipulator into motion planning tasks, it is essential to have a comprehensive grasp of its arm angle and singularities. In this paper, we use the screw dependency approach with a novel combinatorial technique and Grassmann geometry of lines to identify and categorize, for the first time, the kinematic singularities of YuMi based on simple geometrical conditions. This methodology allows for a systematic and clear understanding of the robot’s singular configurations. In addition, we provide the definition of the arm angle used by ABB and a formula for the angle calculation. Then, we describe the representation singularity, and explain the algorithmic singularities that are related to the arm angle. • A new approach for analyzing the kinematic singularities of 7-DOF robot arms was proposed. • The singularities of ABB’s 7-axis YuMi were thoroughly analyzed for the first time. • The geometric conditions for each kinematic singularity were described. • A formula for the arm angle of YuMi was provided for the first time. • A comparison with the singularities of KUKA’s iiwa was presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".