Dexterous manipulation of construction tools using anthropomorphic robotic hand
Bibliographic record
Abstract
Emerging studies are utilizing reinforcement learning (RL) and imitation learning (IL) to control large-scale robots in heavy construction tasks. There is limited attention given to the automation of delicate tasks typically performed manually. This paper proposes the control of an anthropomorphic robotic hand with a high degree of freedom for the manipulation of construction tools in a learning-based approach. For controlling the robotic hand, a simulation-based policy learning framework based on pretraining policies through IL is presented, subsequently fine-tuning them for construction tool manipulation using RL. In experimental trials, six policies are trained for the robotic hand to grasp six different construction tools. The results indicate that each of the learned policies enables the robotic hand to manipulate the corresponding tool with an almost 100% success rate, demonstrating resilience when confronted with tools of different scales. Additionally, the paper showcases the potential for scaling up the fundamental policy for downstream applications.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".