A Deep Reinforcement Learning Solution for the Low Level Motion Control of a Robot Manipulator System
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Bibliographic record
Abstract
Motion planning and control is a necessary aspect of incorporating robots into the real world. There are a variety of different types of control tasks that involve collision avoidance and fine control, that are difficult to program without the use of artificial intelligence (AI), especially in an non-stationary environment. In this paper, one method for applying deep reinforcement learning (RL) to the motion planning of a manipulator robot is described. Using a soft actor-critic (SAC) network, a model is trained to direct the manipulator to various locations so as to avoid colliding either its hand or the object it carries with a game tower. This demonstrates a simple and effective method for training an agent to achieve its goal that generalizes to similar but different environments.
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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 | 0.001 | 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 | 0.000 |
| 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