Accelerating Training of Reinforcement Learning-Based Construction Robots in Simulation Using Demonstrations Collected in Virtual Reality
Bibliographic record
Abstract
The application of construction robots is crucial to mitigate challenges faced by the construction industry, such as labor shortages and low productivity. Reinforcement learning (RL) enables robots to take actions based on observed states, improving flexibility over traditional robots pre-programmed to follow determined sequences of instructions. However, RL-based control is time-consuming to train, hindering the wide adoption of RL-based construction robots. This paper proposes an approach that utilizes expert demonstrations collected from virtual reality to accelerate the RL training of construction robots. For evaluation, we implement the approach for the task of window pickup and installation on a virtual construction site. In our experiment, out of 10 RL agents trained using virtual expert demonstrations, 7 agents converge to an optimal policy faster than the baseline RL agent trained without demonstrations by around 40 epochs, which proves adding expert demonstrations can effectively accelerate the training of robots learning construction tasks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".