A Reinforcement Learning Approach in Assignment of Task Priorities in Kinematic Control of Redundant Robots
Bibliographic record
Abstract
Based on the recent advances of Deep Reinforcement Learning (DRL) and promising results, in this paper, we propose a framework for strict priority assignment in the context of kinematic control of redundant robots. The presented method focuses on redundant robots performing multiple concurrent tasks with potentially conflicting requirements and learns how to re-assign task priorities to ensure critical tasks get executed through smooth transitions. A Deep Q-Network (DQN) reinforcement learning agent is trained to assign the proper strict priorities to a stack of predefined kinematic control tasks (e.g., position control, orientation control, obstacle avoidance control, etc.) in a varying environment. Furthermore, to address the discontinuities in the control law due to the changes in the task priorities, a smoothing algorithm is proposed to ensure continuous reference velocities to the robot’s joints. The proposed method is generic and extendable to a higher number of tasks and can be used when a reordering, swapping, addition, or deletion of tasks is required. The effectiveness of the proposed method is shown in simulation on a 5-DoF planar manipulator and a 7-DoF planar bipedal robot. The results show that the DRL agent is successful in assigning the correct hierarchy of tasks at each robot’s state based on the global goal of the robot.
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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 | 0.002 |
| 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 | 0.001 |
| 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".