A context-aware real-time human-robot collaborating reinforcement learning-based disassembly planning model under uncertainty
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Bibliographic record
Abstract
Herein, we present a real-time multi-agent deep reinforcement learning model as a disassembly planning framework for human–robot collaboration. This disassembly plan optimises sequences to minimise operation time and the disassembling costs of end-of-life (EoL) products. Combining different data-driven decision-making tools, the plan aims to handle the complexities and uncertainties of disassembly tasks. Based on the physical features and geometric limitations of EoL product components, we calculate product disassembly difficulty scores. Subsequently, the deep reinforcement learning model integrates these scores into planning process. The model allocates tasks in real time according to the online conditions of the human operator, cobot, and product, enabling the model to cope with uncertainties that may change the process routine. We also present different scenarios wherein a cobot collaborates with human operators with different skill levels. To evaluate model performance, we compare it with baseline models in terms of the convergence time and incorporated disassembly features. The analysis indicates that our model converges three times faster than a baseline model applied to the same case study. Moreover, our model includes more features of the disassembly problem in its decision-making process than any other baseline model.
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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 | 0.001 | 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