Autonomous modular construction strategy using robotized crane based on deep learning and reinforcement learning
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
Modular construction offers significant advantages including faster construction time, higher quality control and less environmental impact. To further enhance its advantages, advanced robotic construction technologies are being developed. This research develops an automated modular construction framework that incorporates the robotic kinematics, deep learning and deep reinforcement learning using a robotized crane. The proposed modular construction strategy utilizes YOLOv5-S for modular container identification and localization. An improved proximal policy optimization (PPO-I) is developed and implemented in this strategy for collision-free three-dimensional (3D) lifting path planning and modular container transportation. States and rewards of the PPO-I and robot kinematics design of a real mobile crane are developed. The feasibility of the proposed modular construction strategy is verified through four case studies in 3D virtual environments. More than 97% success rate is observed meaning that the proposed strategy can be implemented in the robotized crane to localize the modular container and transport it to the target position with collision avoidance. The results indicate the potential of the proposed robotic-assisted modular construction strategy in the field of automated construction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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