Optimizing 7-DOF Robot Manipulator Path Using Deep Reinforcement Learning Techniques
Bibliographic record
Abstract
This paper proposes three different Deep Rein-forcement Learning (DRL) techniques to find a free-obstacle path for a 7-DOF Robot Manipulator (RM). The robot is the Kinova Jaco Assistive Robot arm; its DH parameters and kinematics are presented. The suggested DRL methods are Deep Q-Network (DQN), Actor-Critic (AC), and Proximal Policy Optimization (PPO) algorithms. The environment, state space, action space, and reward function are defined to suit all proposed methods. The experiment is run to validate the performance of these methods in finding the path for the RM in two different environments. These environments differ in obstacles and complexity. The reward and average reward evaluation were used to evaluate the proposed methods. The results showed that the three models found the path in the suggested environments. The comparison between these methods is presented and discussed in this paper. The result discussion clarified the superiority of the PPO method in finding the free-obstacle path in a complex environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".