Autonomous construction of structures in a dynamic environment using Reinforcement Learning
Bibliographic record
Abstract
This paper presents an adaptive approach based on the Reinforcement Learning (RL) method to manipulate and transport parts and also assemble 3-D structures in a moderately constrained and dynamic environment using a quad-rotor. Nowadays, complex construction tasks using mobile robots are characterized by two fundamental problems such as task planning and motion planning. However, to obtain the task and path planning that define a specific sequence of operations for construction of a given structure is generally very complex. In this context, we propose and investigate a system in which an aerial robot learns the assembly and construction tasks of multiple 3-D structures. This process involves the learning of the sequence of maneuvers of a vehicle, the assembly sequence of the parts and also the correct types of structural elements for each assembly point of the structure. A heuristic search algorithm is used in the learning process to find the optimal path for the quad-rotor so that its navigation through the dynamic environment is performed. The experimental results show that a 3-D structure can be built using the task planning approach derived from a learning algorithm combined with a heuristic search method.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".