Autonomously Reconfigurable Robot Coupled with Intelligent Vision and Control System for Rapid Runtime Adaptation
Bibliographic record
Abstract
The widespread integration of robotic technology across diverse sector’s necessitates solutions that embody both adaptability and speed. However, existing systems often exhibit a trade-off between these attributes, lacking swift adaptability. This paper introduces a novel self-reconfiguration system that addresses this challenge. Employing a scissor mechanism for rapid adjustment and an intelligent binocular vision driven by computer vision and reinforcement learning algorithms, the proposed system facilitates swift reconfiguration while effectively managing workload. Real-time perception and understanding of the surroundings enable the robot to promptly identify optimal dimensions and adjust its configuration accordingly. This capability empowers the robot to rapidly respond to new tasks without manual intervention, thereby augmenting its versatility and agility. Experimental validation demonstrates the system’s adeptness in navigating passages of varying widths in under a second, underscoring the efficacy of our approach in training the robot to adeptly adapt to diverse environmental conditions.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".