Review of task allocation for human-robot collaboration in assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to the high cost pressure and the increasing variant diversity, the cooperation of humans and robots represents a promising technological solution to achieve higher flexibility and efficiency in assembly. It is therefore attracting significant interest from both researchers and practitioners. As a result, numerous reviews have been published addressing different aspects of human-robot collaboration, such as safety, interaction modalities, programming, and applications. However, in this paper, for the first time, the aspect of task allocation for collaborative assembly is methodologically examined through a systematic literature review. This paper presents the current state of the art in task allocation approaches, investigates the criteria for deciding on a suitable task assignment, and discusses challenges and future research needs. After filtering the 521 publications that resulted from the initial search process, 37 relevant publications were included in the analysis and grouped into a proposed classification consisting of two main categories, static and dynamic task allocation approaches. Based on the results of the literature review, this paper presents a reference model for human-robot collaborative assembly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it