Training of construction robots using imitation learning and environmental rewards
Why this work is in the frame
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Bibliographic record
Abstract
Construction robots are challenging the paradigm of labor-intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high-quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed. First, traditional control using controllers has been replaced with vision-based hand gesture control for intuitive demonstration collection. Second, a novel method that integrates both demonstrations and simple environmental rewards is proposed to strike a balance between imitation and exploration. To achieve this goal, a two-step training process is proposed. In the first step, an intuitive demonstration collection platform using virtual reality is utilized. Second, a learning algorithm is used to train a policy for construction tasks. Experimental results demonstrate that combining IL with environmental rewards can significantly accelerate the training, even with limited demonstration data.
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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