Integration and Assessment of Multiple Mobile Manipulators in a Real-World Industrial Production Facility
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a large-scale research experiment carried out within the TAPAS project, where multiple mobile manipulators were integrated and assessed in an industrial context. We consider an industrial scenario in which mobile manipulators naturally extend automation of logistic tasks towards assistive ones. In the experiment, we included tasks such as preparatory and post-processing work, e.g. pre-assembly or machine tending with inherent quality control. In the experiment, we deployed the two heterogeneous mobile manipulators Little Helper and omniRob in a production scenario at Grundfos A/S, a manufacturer of water circulation pumps, in Denmark. The experiment showed that mobile manipulation is at a level of technology readiness that will allow industrial application in the near future. Despite challenges indicated later in the paper, the research efforts presented do show that research is on the right track on transferring mobile manipulation from research to industry.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it