Optimizing Robotic Arm Learning: Curiosity-Driven Deep Deterministic Policy Gradient
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores the application of the Reinforcement Learning (RL) in training robotic arms, particularly using the Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by a curiosity- driven mechanism. Robotic arms have various real-life applications, such as in the surgeries and assistive technologies. However, collecting the large- scale real-world data is costly and impractical, making simulation environments essential for optimization. The DDPG, well-suited for continuous action spaces, was employed to improve the robotic arm’s precision and adaptability. Integrating a curiosity mechanism allowed the system to explore and learn more efficiently, significantly improving the training time and success rate. The results demonstrate a 12% reduction in training time and an 18% increase in the success rate when using curiosity- driven exploration. These findings suggest that the enhanced DDPG algorithm not only accelerates learning but also enables better task execution, offering a promising approach for the real-world robotic applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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