Human-Robot Collaborative Workflow for Remote Decommissioning and Demolition
Bibliographic record
Abstract
The demand for new deconstruction and demolition approaches is escalating as structures built in 20th century development booms approach their end of life. Rehabilitation and careful deconstruction approaches are increasingly economically and environmentally motivating. For example, in Ontario, Canada multi-decade efforts to decommission nuclear power plants are challenging teams of engineers, researchers, venders, and laborers. In these hazardous scenarios, classical heavy demolition approaches are not an option, and the asset owners find that the costly development of novel workflows and technologies to plan and undergo these deconstruction operations is the only option. These trends present construction researchers with an opportunity to develop technologies and processes to achieve deconstruction project goals with improved efficiency, certainty, and safety. This paper presents a modular framework for remote human-robot collaboration for waste management in decommissioning and demolition. The proposed framework includes robotic platform reality data capture, scan processing (e.g., segmentation, surface estimation, and recognition), gamified waste packing in virtual reality (VR), and packing plan execution. A comprehensive review of state-of-the-art technologies of each module is explored from the standpoint of applicability to deconstruction and demolition. Then, an autonomous robotic platform for reality data capture is presented. A reconfigurable semi-automated VR platform for waste packing optimization is presented as an example of this process workflow in the context of remote deconstruction and demolition. Finally, the ideas of robotic packing plan execution are discussed as future work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".